Reinforcement Learning helps with Investor Decisions
RL is used to provide optimal investment decisions for a typical home owner
• 829 min read
Investment_Industry Reinforcement_Learning DDPG OpenAI Gym TensorFlow TF-Agents
- 1. Introduction
- 2. Solution Proposal
- 3. Implementation of the Solution
1. Introduction
This post describes a simple way of how Reinforcement Learning (RL) can be applied to the problem of "homeowner investment". This does not represent a sophisticated model. However, its simplicity is beneficial allowing us to convey the essentials. This model allows an experimentor to play with various scenarios in a form of "what-if" analysis. It allows the experimentor to get a 'feel' for the factors involved in long-term investment for a typical home owner (i.e. not for a professional investor). The following aspects are included:
- starting age of the investor
- retirement age of the investor
- inflation rate
- tax rate
- investor's salary at the time the investment starts
- expected expenses of the investor
- size of a home mortgage
- mortgage interest rate
- investment return interest rate
- tax deferred contributions limit
- company match on tax deferred contributions
2. Solution Proposal
Investment decisions are sequential by nature. Furthermore, an optimal decision in the present may turn out not to be optimal over the longer term. Then there are the complexities of the investment landscape like varying interest rates, inflation, and other economic uncertainties. The ideal tool for this kind of problem is, of course, Reinforcement Learning.
The solution requires the setup of a digital twin of the investor. In RL parlance this model of the investor is called an environment. The environment contains states which are modified by applying actions to it.
The present solution was inspired by the suggestion of Jeff Heaton, McKelvey School of Engineering, Washington University in St. Louis.
We will choose the following states for the environment:
$$ \Large \begin{align} & s_1=\text{Age} \\ & s_2=\text{Salary} \\ & s_3=\text{Home Value} \\ & s_4=\text{Home Loan Amount} \\ & s_5=\text{Home Required Payment} \\ & s_6=\text{Tax Deferred Savings} \\ & s_7=\text{Taxable Savings} \\ \end{align} $$The following actions (applied monthly) will be setup to influence the environment/investor:
$$ \Large \begin{align} & a_1=\text{Home Loan Payment Amount} \\ & a_2=\text{Tax Deferred Deposit Amount} \\ & a_3=\text{Taxable Deposit Amount} \\ & a_4=\text{Luxury Spending Amount} \\ \end{align} $$The values of all states and actions will be in terms of millions, i.e. M$ (mega-dollars) in the case of monetary amounts. Neural networks work better with smaller numbers.
The model of the environment/investor will have the following parameters:
$$ \Large \begin{align} & \theta_1=\text{Investor Start Age} \\ & \theta_2=\text{Investor Retire Age} \\ & \theta_3=\text{Inflation Rate} \\ & \theta_4=\text{Tax Rate} \\ & \theta_5=\text{Low Value of Investor's Salary when investment starts} \\ & \theta_6=\text{High Value of Investor's Salary when investment starts} \\ & \theta_7=\text{Expenses of the Investor (as fraction of salary)} \\ & \theta_8=\text{Low Value of House Price (as multiple of salary)} \\ & \theta_9=\text{High Value of House Price (as multiple of salary)} \\ & \theta_{10}=\text{Mortgage Interest Rate} \\ & \theta_{11}=\text{Investment Return Rate} \\ & \theta_{12}=\text{Tax Deferred Contributions Limit (government's annual cap)} \\ & \theta_{13}=\text{Company Match on Tax Deferred Contributions} \\ \end{align} $$Rather than having the investor's salary directly as a parameter, we will have two parameters, one for the low value and one for the high value. This makes the model more flexible, in particular during training. The same applies for the two parameters used for the house price. In addition, we will add some variability for the interest rates. The latter variabilities may be considered disturbances experienced by the investor. The agent has the ability to incorporate such disturbances during its operation.
3. Implementation of the Solution
To implement the environment we use the OpenAI Gym tools. We will use the TF-Agents python library from Google to implement the agent. Because we have continuous actions we will use the Deep Deterministic Policy Gradients (DDPG) network as a function approximator for the agent.
This implementation allows the agent to allocate the investor's income between several targets, namely, tax-deferred savings, taxable savings, buying luxury items, and paying off the mortgage. The goal is to maximize net worth at the end of the investment horizon.
The code will run on the Google Colab platform. To start with, we install the python packages needed.
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!pip install -q 'gym==0.10.11'
!pip install -q tf-agents
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Import the packages needed:
import base64
import IPython
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import math
import numpy as np
import matplotlib as mpl
import pickle
import tensorflow as tf
from tf_agents.agents.ddpg import actor_network
from tf_agents.agents.ddpg import critic_network
from tf_agents.agents.ddpg import ddpg_agent
from tf_agents.agents.dqn import dqn_agent
from tf_agents.drivers import dynamic_step_driver
from tf_agents.environments import suite_gym
from tf_agents.environments import tf_py_environment
from tf_agents.eval import metric_utils
from tf_agents.metrics import tf_metrics
from tf_agents.networks import q_network
from tf_agents.policies import random_tf_policy
from tf_agents.replay_buffers import tf_uniform_replay_buffer
from tf_agents.trajectories import trajectory
from tf_agents.trajectories import policy_step
from tf_agents.utils import common
import gym
from gym import spaces
from gym.utils import seeding
from gym.envs.registration import register
class Investor(gym.Env):
NUM_STATE_ELEMENTS = 7
STATES = [
#states proper:
'sta_age','sta_salary','sta_home_value','sta_home_loan','sta_home_req_payment','sta_save_tax_def','sta_save_taxable',
#append to self.state_log in step() function for visualization purposes:
"expenses","home_actual_pmt","deposit_taxable","deposit_tax_def","luxury_spend","net_worth",
#append action vector for visualization purposes:
"a_home_loan", "a_save_tax_def", "a_save_taxable", "a_luxury"
]
MEG = 1.0e6 #for normalization
# STATES
STA_AGE = 0
STA_SALARY = 1
STA_HOME_VALUE = 2
STA_HOME_LOAN = 3
STA_HOME_REQ_PAYMENT = 4
STA_SAVE_TAX_DEF = 5
STA_SAVE_TAXABLE = 6
# ACTIONS
NUM_ACTION_ELEMENTS = 4
ACT_HOME_LOAN = 0
ACT_SAVE_TAX_DEF = 1
ACT_SAVE_TAXABLE = 2
ACT_LUXURY = 3
# PARAMETERS
PAR_START_AGE = 18
PAR_RETIRE_AGE = 80
PAR_INFLATION = 0.05/12.0 #per month
PAR_TAX_RATE = 0.142/12.0 #per month
PAR_SALARY_LOW = 40000 #per year
PAR_SALARY_HIGH = 60000 #per year
PAR_EXPENSES = 0.6 #(of salary)
PAR_HOUSE_MULT_LOW = 1.5 #(of salary)
PAR_HOUSE_MULT_HIGH = 4.0 #(of salary)
PAR_MORT_INT = 0.05/12.0 #per month
PAR_INVEST_INT = 0.065/12.0 #per month
PAR_TAX_DEF_DEPOSIT_CAP = 19000 #per year
PAR_TAX_DEF_COMPANY_MATCH = 1.05 #company match
PAR_ACTION_SPACE_LOW = 0.0 #in millions, i.e. $0
PAR_ACTION_SPACE_HIGH = 0.01 #in millions, i.e. $10,000
PAR_OBSERVATION_SPACE_LOW = 0.0 #in millions, i.e. $0
PAR_OBSERVATION_SPACE_HIGH = 10.0 #in millions, i.e. $10,000,000
def __init__(self, goal_velocity=0):
self.verbose = False
self.viewer = None
self.inflation = None
self.invest_return = None
self.year_tax_def_deposit_left = None
self.expenses = None
self.ke_expenses = None
self.ke_home_actual_pmt = None
self.ke_deposit_taxable = None
self.ke_deposit_tax_def = None
self.ke_luxury_spend = None
self.ke_networth = None
self.action_space = spaces.Box(
low=Investor.PAR_ACTION_SPACE_LOW,
high=Investor.PAR_ACTION_SPACE_HIGH,
shape=(Investor.NUM_ACTION_ELEMENTS,),
dtype=np.float32
)
self.observation_space = spaces.Box(
low=Investor.PAR_OBSERVATION_SPACE_LOW,
high=Investor.PAR_OBSERVATION_SPACE_HIGH,
shape=(Investor.NUM_STATE_ELEMENTS,),
dtype=np.float32
)
self.seed()
self.reset()
self.state_log = []
def seed(self, seed=None):
self.np_random, seed = seeding.np_random(seed) #rng, seed
return [seed]
def _calc_net_worth(self):
worth = self.state[Investor.STA_HOME_VALUE] - self.state[Investor.STA_HOME_LOAN] # 'under-water' when negative
worth += self.state[Investor.STA_SAVE_TAX_DEF]
worth += self.state[Investor.STA_SAVE_TAXABLE]
return worth
def step(self, action):
if self.verbose: print(f"step with action: {action}")
# self.last_action = action #not used explicitly
sta_age = self.state[Investor.STA_AGE]
sta_salary = self.state[Investor.STA_SALARY]
sta_home_value = self.state[Investor.STA_HOME_VALUE]
sta_home_loan = self.state[Investor.STA_HOME_LOAN]
sta_home_req_payment = self.state[Investor.STA_HOME_REQ_PAYMENT]
act_save_tax_def = action[Investor.ACT_SAVE_TAX_DEF]*1e6
act_home_loan = action[Investor.ACT_HOME_LOAN]*1e6
act_save_taxable = action[Investor.ACT_SAVE_TAXABLE]*1e6
act_luxury = action[Investor.ACT_LUXURY]*1e6
net1 = self._calc_net_worth()
remaining_salary = sta_salary
if self.verbose: print(f"Available Salary: {sta_salary:,.2f}")
# pct_home_loan, pct_save_tax_def, pct_save_taxable, pct_luxury = self._eval_action(action, sta_home_req_payment) #---
# 1. Expenses
current_expenses = self.expenses*sta_salary
remaining_salary -= current_expenses
if self.verbose:
print(f"Expenses: {current_expenses:,.2f}")
print(f"---Remaining Salary after expenses: {remaining_salary:,.2f}")
# 2. Tax deferred deposit action
# my_tax_def_deposit = min(pct_save_tax_def*sta_salary, remaining_salary)
my_tax_def_deposit = min(act_save_tax_def, remaining_salary) #---
my_tax_def_deposit = min(my_tax_def_deposit, self.year_tax_def_deposit_left) #Govt CAP
self.year_tax_def_deposit_left -= my_tax_def_deposit
remaining_salary -= my_tax_def_deposit
deposit_tax_def = Investor.PAR_TAX_DEF_COMPANY_MATCH*my_tax_def_deposit
self.state[Investor.STA_SAVE_TAX_DEF] += int(deposit_tax_def)
if self.verbose:
print(f"IRA Deposit (deposit_tax_def): {deposit_tax_def:,.2f}")
print(f"---Remaining Salary after deposit_tax_def: {remaining_salary:,.2f}")
# 3. Tax
tax = Investor.PAR_TAX_RATE*remaining_salary
remaining_salary -= tax
if self.verbose:
print(f"tax: {tax:,.2f}")
print(f"---Remaining Salary after tax: {remaining_salary:,.2f}")
# 4. Home
# home_actual_payment = min(pct_home_loan*sta_salary, remaining_salary)
if sta_home_loan > 0:
home_actual_payment = min(act_home_loan, remaining_salary) #---
ipart = Investor.PAR_MORT_INT*sta_home_loan
ppart = home_actual_payment - ipart
principal = int(sta_home_loan - ppart)
if principal <= 0:
principal = 0
self.state[Investor.STA_HOME_REQ_PAYMENT] = 0
elif home_actual_payment < sta_home_req_payment:
self.late_count += 1
if self.late_count > 15: #foreclose
sell = (sta_home_value - principal)/2
sell -= 20000 #fees for selling
sell = max(sell, 0)
self.state[Investor.STA_SAVE_TAXABLE] += sell
principal = 0
#sta_home_value = 0 #nothing done with it
self.expenses += .3 #for rent
self.state[Investor.STA_HOME_REQ_PAYMENT] = 0
self.state[Investor.STA_HOME_VALUE] = 0
#self.state[Investor.STA_HOME_LOAN] = 0 #see couple lines down
if self.verbose: print(f"Foreclosure!!")
else:
late_fee = 0.10*sta_home_req_payment
principal += late_fee
if self.verbose: print(f"Late Fee: {late_fee:,.2f}")
self.state[Investor.STA_HOME_LOAN] = principal
remaining_salary -= home_actual_payment
else: home_actual_payment = 0.0 #---
if self.verbose:
print(f"sta_home_loan: {sta_home_loan:,.2f}")
print(f"Home Payment: {home_actual_payment:,.2f}")
print(f"---Remaining Salary after home payment: {remaining_salary:,.2f}")
# 5. Taxable savings
# actual_savings = pct_save_taxable*remaining_salary
actual_savings = min(act_save_taxable, remaining_salary) #---
self.state[Investor.STA_SAVE_TAXABLE] += actual_savings
remaining_salary -= actual_savings
if self.verbose:
print(f"actual_savings: {actual_savings:,.2f}")
print(f"---Remaining Salary after actual_savings (goes to Luxury): {remaining_salary:,.2f}")
# 6. Luxury spend
# luxury_spend = remaining_salary
luxury_spend = min(act_luxury, remaining_salary) #updated lower #---
remaining_salary -= luxury_spend
if self.verbose:
print(f"luxury_spend: {luxury_spend:,.2f}")
print(f"---Remaining Salary after luxury_spend: {remaining_salary:,.2f}")
# 7. Investment income
return_taxable = self.invest_return*self.state[Investor.STA_SAVE_TAXABLE]
return_tax_adv = self.invest_return*self.state[Investor.STA_SAVE_TAX_DEF]
return_taxable *= 1 - Investor.PAR_TAX_RATE
self.state[Investor.STA_SAVE_TAXABLE] += return_taxable
self.state[Investor.STA_SAVE_TAX_DEF] += return_tax_adv
# Yearlies
if sta_age>0 and sta_age % 12 == 0: self.perform_yearly()
# Monthlies
self.state[Investor.STA_AGE] += 1
# Time to retire?
done = self.state[Investor.STA_AGE] > (Investor.PAR_RETIRE_AGE*12)
# Calculate reward
net2 = self._calc_net_worth()
reward = net2 - net1
# Track progress
if self.verbose:
print(f"Networth: {net2:,.2f}")
print(f"*** End Step {self.step_num}: \nState={self.state}, Reward={reward}\n")
log_entry = self.state + \
[current_expenses,home_actual_payment,actual_savings,my_tax_def_deposit,luxury_spend,net2] + \
list(action)
self.state_log.append(log_entry); #print(f"******* log_entry={log_entry}")
# Capture so that render() can get them
self.ke_expenses = current_expenses
self.ke_home_actual_pmt = home_actual_payment
self.ke_deposit_taxable = actual_savings
self.ke_deposit_tax_def = my_tax_def_deposit
self.ke_luxury_spend = luxury_spend
self.ke_networth = net2
self.step_num += 1
norm_state = [x/Investor.MEG for x in self.state] #normalize
return norm_state, reward/Investor.MEG, done, {}
def perform_yearly(self):
self.inflation = Investor.PAR_INFLATION + self.np_random.normal(loc=0, scale=1e-2)
self.invest_return = Investor.PAR_INVEST_INT + self.np_random.normal(loc=0, scale=1e-2) #loc is mean, scale is stdev
self.year_tax_def_deposit_left = Investor.PAR_TAX_DEF_DEPOSIT_CAP
self.state[Investor.STA_SALARY] = int((1 + self.inflation)*self.state[Investor.STA_SALARY])
self.state[Investor.STA_HOME_VALUE] = int((1 + self.inflation)*self.state[Investor.STA_HOME_VALUE])
def reset(self):
self.expenses = Investor.PAR_EXPENSES
self.late_count = 0
self.step_num = 0
self.state = [0]*Investor.NUM_STATE_ELEMENTS
self.state_log = []
self.ke_expenses = 0
self.ke_home_actual_pmt = 0
self.ke_deposit_taxable = 0
self.ke_deposit_tax_def = 0
self.ke_luxury_spend = 0
self.ke_networth = 0
salary = float(self.np_random.randint(low=Investor.PAR_SALARY_LOW, high=Investor.PAR_SALARY_HIGH)) #discrete-uniform (inclusive, exclusive)
house_mult = self.np_random.uniform(low=Investor.PAR_HOUSE_MULT_LOW, high=Investor.PAR_HOUSE_MULT_HIGH) #uniform (inclusive, exclusive)
value = round(house_mult*salary)
p = (0.9*value)
i = Investor.PAR_MORT_INT
n = 30*12
m = float(int(p * ( i * (1 + i)**n ) / ( (1 + i)**n - 1)))
self.state[Investor.STA_AGE] = Investor.PAR_START_AGE*12
self.state[Investor.STA_SALARY] = salary/12.0
self.state[Investor.STA_HOME_VALUE] = value
self.state[Investor.STA_HOME_LOAN] = p
self.state[Investor.STA_HOME_REQ_PAYMENT] = m
self.year_tax_def_deposit_left = Investor.PAR_TAX_DEF_DEPOSIT_CAP #19000
self.perform_yearly()
return np.array(self.state)
def render(self, mode='human'):
# sta_age = self.state[Investor.STA_AGE]
# sta_salary = self.state[Investor.STA_SALARY]*12
# sta_home_value = self.state[Investor.STA_HOME_VALUE]
# sta_home_loan = self.state[Investor.STA_HOME_LOAN]
# sta_home_req_payment = self.state[Investor.STA_HOME_REQ_PAYMENT]
# sta_save_tax_def = self.state[Investor.STA_SAVE_TAX_DEF]
# sta_save_taxable = self.state[Investor.STA_SAVE_TAXABLE]
# net_worth = self._calc_net_worth()
return (self.ke_expenses,self.ke_home_actual_pmt,self.ke_deposit_taxable,self.ke_deposit_tax_def,self.ke_luxury_spend,self.ke_networth)
def close(self):
pass
Register the investor:
env_name = 'investor-v0'
register(
id=env_name,
entry_point=f'{__name__}:Investor'
)
3.1 Agent
Now we will implement the agent.
We will work with a graphical representation of our Investor problem, rather than to just ramble on with words. This will enhance the description. The graphic will also include some TF-Agents specifics. Here is the representation:
3.1.1 Hyperparameters
The following hyperparamaters need to be specified to train the agent:
num_iterations = 60000
initial_collect_steps = 1000 #How many initial random steps, before training start, to collect initial data.
collect_steps_per_iteration = 50 #How many steps should we run each iteration to collect data from.
replay_buffer_max_length = 100000 #How much data should we store for training examples.
batch_size = 64
#learning_rate = 1e-4 #How often should the program provide an update.
log_interval = 2500 #log entry occurs each 2500 steps
num_eval_episodes = 100 #episodes used for each evaluation
eval_interval = 5000 #evaluation occurs once every 5000 steps
train_py_env = suite_gym.load(env_name)
eval_py_env = suite_gym.load(env_name)
Next we wrap the python environments with TensorFlow environments:
train_env = tf_py_environment.TFPyEnvironment(train_py_env)
eval_env = tf_py_environment.TFPyEnvironment(eval_py_env)
To provide a continuous action space we will use the Deep Deterministic Policy Gradients (DDPG) algorithm. [Cite:lillicrap2015continuous] The algorithm uses two neural networks. The one, called an actor, acts as the agent and predicts the expected reward for a given value of the action. The second neural network, called a critic, is trained to predict the accuracy of the actor-network. This technique of training two neural networks in parallel that operate adversarially to each other is quite popular.
The following code creates the actor and critic neural networks:
actor_fc_layers = (400, 300)
critic_obs_fc_layers = (400,)
critic_action_fc_layers = None
critic_joint_fc_layers = (300,)
ou_stddev = 0.2
ou_damping = 0.15
target_update_tau = 0.05
target_update_period = 5
dqda_clipping = None
td_errors_loss_fn = tf.compat.v1.losses.huber_loss
gamma = 0.995
reward_scale_factor = 1.0
gradient_clipping = None
actor_learning_rate = 1e-4
critic_learning_rate = 1e-3
debug_summaries = False
summarize_grads_and_vars = False
actor_net = actor_network.ActorNetwork(
input_tensor_spec= train_env.time_step_spec().observation,
output_tensor_spec= train_env.action_spec(),
fc_layer_params= actor_fc_layers)
critic_net_input_specs = (train_env.time_step_spec().observation, train_env.action_spec())
critic_net = critic_network.CriticNetwork(
input_tensor_spec= critic_net_input_specs,
observation_fc_layer_params= critic_obs_fc_layers,
action_fc_layer_params= critic_action_fc_layers,
joint_fc_layer_params= critic_joint_fc_layers,
)
global_step = tf.compat.v1.train.get_or_create_global_step(); print(global_step)
<tf.Variable 'global_step:0' shape=() dtype=int64, numpy=0>
tf_agent = ddpg_agent.DdpgAgent(
time_step_spec= train_env.time_step_spec(),
action_spec= train_env.action_spec(),
actor_network= actor_net,
critic_network= critic_net,
actor_optimizer=tf.compat.v1.train.AdamOptimizer(
learning_rate= actor_learning_rate),
critic_optimizer=tf.compat.v1.train.AdamOptimizer(
learning_rate= critic_learning_rate),
ou_stddev= ou_stddev,
ou_damping= ou_damping,
target_update_tau= target_update_tau,
target_update_period= target_update_period,
dqda_clipping= dqda_clipping,
td_errors_loss_fn= td_errors_loss_fn,
gamma= gamma,
reward_scale_factor= reward_scale_factor,
gradient_clipping= gradient_clipping,
debug_summaries= debug_summaries,
summarize_grads_and_vars= summarize_grads_and_vars,
train_step_counter= global_step)
tf_agent.initialize()
def compute_avg_return(env, pol, num_episodes=10):
print(f"... computing avg return with num_episodes={num_episodes}")
total_return = 0.0
for _ in range(num_episodes):
tstep = env.reset()
episode_return = 0.0
while not tstep.is_last():
pstep = pol.action(tstep)
tstep = env.step(pstep.action)
episode_return += tstep.reward
total_return += episode_return
avg_return = total_return / num_episodes
return avg_return.numpy()[0]
replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
data_spec=tf_agent.collect_data_spec,
batch_size=train_env.batch_size,
max_length=replay_buffer_max_length)
collect_driver = dynamic_step_driver.DynamicStepDriver(
env=train_env,
policy=tf_agent.collect_policy,
observers=[replay_buffer.add_batch],
num_steps=collect_steps_per_iteration)
# Initial data collection
collect_driver.run()
dataset = replay_buffer.as_dataset(
sample_batch_size=batch_size,
num_steps=2,
num_parallel_calls=3).prefetch(3)
iterator = iter(dataset);
WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tf_agents/drivers/dynamic_step_driver.py:203: calling while_loop_v2 (from tensorflow.python.ops.control_flow_ops) with back_prop=False is deprecated and will be removed in a future version. Instructions for updating: back_prop=False is deprecated. Consider using tf.stop_gradient instead. Instead of: results = tf.while_loop(c, b, vars, back_prop=False) Use: results = tf.nest.map_structure(tf.stop_gradient, tf.while_loop(c, b, vars)) WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/autograph/operators/control_flow.py:1218: ReplayBuffer.get_next (from tf_agents.replay_buffers.replay_buffer) is deprecated and will be removed in a future version. Instructions for updating: Use `as_dataset(..., single_deterministic_pass=False) instead.
# Setup Checkpointer and PolicySaver
model = 'policy7'
from pathlib import Path
# Checkpointer
checkpoint_dir = Path(f'{base_dir}/{model}/checkpoint'); print(checkpoint_dir)
train_checkpointer = common.Checkpointer(
ckpt_dir=checkpoint_dir,
max_to_keep=15,
agent=tf_agent,
policy=tf_agent.policy,
replay_buffer=replay_buffer,
global_step=global_step
)
# PolicySaver
from tf_agents.policies import policy_saver
policy_dir = Path(f'{base_dir}/{model}/policy'); print(policy_dir)
tf_policy_saver = policy_saver.PolicySaver(policy=tf_agent.policy)
# Returns
returns_pkl_file = Path(f'{base_dir}/{model}/returns.pkl'); print(returns_pkl_file)
# Losses
losses_pkl_file = Path(f'{base_dir}/{model}/losses.pkl'); print(losses_pkl_file)
/content/gdrive/My Drive/RL/TF-Agents/InvestorProject/policy7/checkpoint /content/gdrive/My Drive/RL/TF-Agents/InvestorProject/policy7/policy /content/gdrive/My Drive/RL/TF-Agents/InvestorProject/policy7/returns.pkl /content/gdrive/My Drive/RL/TF-Agents/InvestorProject/policy7/losses.pkl
# Restore checkpoint
# For this to work, the whole set of objects should be recreated the same way as when the checkpoint was created.
train_checkpointer.initialize_or_restore()
global_step = tf.compat.v1.train.get_global_step()
tf.print(global_step)
45000
# Restore policy
# The policy can be loaded without having any knowledge of what agent or network was used to create it. This makes deployment of the policy much easier.
# saved_policy = tf.compat.v2.saved_model.load(str(policy_dir))
# Restore returns
try:
with open(returns_pkl_file, "rb") as fp:
returns = pickle.load(fp); print(returns)
except FileNotFoundError:
print("... file does not exist yet")
# Restore losses
try:
with open(losses_pkl_file, "rb") as fp:
losses = pickle.load(fp); print(losses)
except FileNotFoundError:
print("... file does not exist yet")
[0.91741705, 1.0443436, 0.5601131, 0.5663353, 0.8625177, 0.8818096, 0.8853596, 0.8716588, 0.9818827, 0.86259454] [7.8042765, 103.78926, 6.754154e-06, 5.447705e-06, 26.857994, 3.3991128e-06, 4.049052e-06, 6.3535313e-06, 7.7142754, 4.555776e-06, 3.7468596, 1.9542274e-06, 3.6968834, 1.018519e-06, 2.1366839e-06, 1.8449174e-06, 1.872554e-06, 1.931839e-06, 9.550635e-06, 1.582448e-06, 9.777302e-07, 1.5543576e-06, 1.3509302e-06, 8.961124e-06, 6.24493, 7.583905, 11.29204, 4.1514963e-06, 7.769459e-06, 6.5711233e-06, 3.1543289e-06, 7.3859974e-06, 6.497456e-06, 5.7303578e-06, 6.862789e-06, 6.0875504e-06, 2.9932685e-06, 8.895381e-06, 3.443821, 5.857546e-06, 1.6243685e-05, 5.527584e-06, 7.258278e-06, 7.5722137e-06, 9.8756245e-06, 1.3617364e-05, 8.075551e-06, 29.615828, 7.0937635e-06, 54.53383, 7.586044e-06, 8.518463e-06, 1.3101062e-05, 1.14422955e-05, 7.0283636e-06, 1.2245599e-05, 1.9408739e-05, 1.000139e-05, 5.797842e-06, 7.668658e-06, 5.1853267e-06, 0.08683617, 5.327106e-06, 4.424257e-06, 1.7328364e-05, 0.28813052, 1.4615314e-05, 1.0835444e-05, 4.9013206e-06, 26.867609, 4.259577, 5.6288886e-06, 5.130112e-06, 1.109976e-05, 6.963149e-06, 5.850351e-06, 8.789614e-06, 3.1798986e-06, 1.4627491e-05, 4.0488208e-06, 6.9979824e-06, 7.565817e-06, 7.2524763e-06, 42.433517, 9.600648, 8.514859e-06, 5.2297623e-06, 0.41393995, 1.3469522e-05, 1.0224745e-05, 1.0260808e-05, 1.8035873e-05, 9.1267575e-06, 2.3432684e-05, 1.4278923e-05, 1.6171613e-05, 1.3743173e-05, 1.0833967e-05, 3.1758702, 8.478351e-06, 1.8365616, 1.1580299e-05, 1.37052575e-05, 1.9118273e-05, 2.5869838e-05, 9.947637, 2.8479162e-05, 1.6077618e-05, 2.6937236e-05, 2.504775e-05, 2.344075e-05, 1.0475509e-05, 1.0184424e-05, 3.691073, 15.556947, 11.668752, 1.6201058e-05, 2.0976302e-05, 1.5866692e-05, 3.286211e-05, 294.0467, 2.4880725e-05, 2.030337e-05, 6.396997, 9.788361, 2.5358138e-05, 195.4715, 3.5588706e-05, 4.6213932e-05, 4.391081e-05, 4.047533e-05, 4.1785945e-05, 4.6704732e-05, 5.1023613e-05, 5.0614985e-05, 3.5396974e-05, 3.1982967e-05, 4.542039e-05, 2.8124463e-05, 2.7846057e-05, 2.9213008e-05, 2.4026249e-05, 2.4061848e-05, 40.78493, 3.6247548e-05, 3.1254385e-05, 3.9916704e-05, 3.9696737e-05, 3.170124e-05, 3.0316109e-05, 4.7458474e-05, 3.674633e-05, 3.1222407e-05, 2.9261868e-05, 30.28704, 3.839527e-05, 30.370512, 1.782566e-05, 2.149121e-05, 7.0274005, 2.3310704e-05, 6.2603254, 54.335796, 3.6027148e-05, 5.8739624e-05, 4.6208526e-05, 3.6053465e-05, 5.3621152e-05, 4.1998086e-05, 4.9909595e-05, 2.9287712e-05, 4.1510993e-05, 45.520847, 2.0065028e-05, 2.2213566e-05, 2.7461523e-05, 2.842477e-05, 2.4665727e-05, 1.544473e-05, 1.5115258e-05, 1.546875e-05, 1.6590126e-05, 2.276602e-05, 1.7158934e-05, 2.2183302e-05, 2.0624124e-05, 4.834627, 1.9047515e-05, 2.3185628e-05, 1.901673e-05, 2.2362789e-05, 1.8066412e-05, 15.852063, 1.7389535e-05, 2.3507775e-05, 7.097016, 1.8260598e-05, 2.3293567e-05, 2.0466818e-05, 11.264241, 1.7418504e-05, 57.694458, 2.7263625, 2.4758843e-05, 2.7368984e-05, 2.9020663e-05, 2.8971648e-05, 2.514618e-05, 2.1717111e-05, 19.805925, 1.9616064e-05, 1.7550225e-05, 1.678074e-05, 2.0136846e-05, 2.0648964e-05, 1.5137039e-05, 99.91959, 1.48449435e-05, 1.2896846e-05, 1.1854244e-05, 1.2659642e-05, 3.7729268, 1.9486815e-05, 1.7476605e-05, 2.5649746e-05, 1.7860246e-05, 4.380585e-05, 2.0577574e-05, 3.1708172e-05, 2.006596e-05, 2.4379211e-05, 1.5446703e-05, 1.42416375e-05, 2.5826415e-05, 1.6027205e-05, 7.6955795, 2.3767494e-05, 2.4399817e-05, 11.720248, 2.3154274e-05, 4.296117e-05, 5.017384e-05, 169.2805, 4.2178777e-05, 4.368893e-05, 3.971944e-05, 3.472001e-05, 4.268744e-05, 0.39642552, 5.1212166e-05, 3.220851e-05, 2.6794814e-05, 4.4287714e-05, 1.6245745, 4.210236, 2.9124161e-05, 4.103789e-05, 3.1954998e-05, 1.7224911e-05, 2.5806523e-05, 23.79231, 2.1818367e-05, 2.3586428e-05, 28.793545, 3.1687836e-05, 4.6774512e-05, 4.753976e-05, 29.108213, 4.4834687e-05, 4.1857522e-05, 4.0902345e-05, 108.00321, 4.093487e-05, 3.628006e-05, 115.35359, 48.15078, 3.481312e-05, 2.7624163e-05, 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# adjust env before training
train_py_env.gym.verbose = False
Investor.PAR_ACTION_SPACE_LOW = 0.0 #in millions, i.e. $0
Investor.PAR_ACTION_SPACE_HIGH = 0.01 #in millions, i.e. $10,000
Investor.PAR_OBSERVATION_SPACE_LOW = 0.0 #in millions, i.e. $0
Investor.PAR_OBSERVATION_SPACE_HIGH = 10.0 #in millions, i.e. $10,000,000
Investor.PAR_START_AGE = 20
Investor.PAR_RETIRE_AGE = 80
Investor.PAR_INFLATION = 0.014/12.0 #for 12 months ended December 2020; https://www.usinflationcalculator.com/inflation/current-inflation-rates/
Investor.PAR_TAX_RATE = 0.22/12.0 #https://www.businessinsider.com/personal-finance/what-tax-bracket-am-i-in-federal-income-tax
Investor.PAR_SALARY_LOW = 50000 #per year
Investor.PAR_SALARY_HIGH = 100000 #per year
Investor.PAR_EXPENSES = 0.57 #of salary
Investor.PAR_HOUSE_MULT_LOW = 1.0 #of salary
Investor.PAR_HOUSE_MULT_HIGH = 5.0 #of salary
Investor.PAR_MORT_INT = 0.03/12.0 #https://www.bankrate.com/finance/interest-rates-forecast/
Investor.PAR_INVEST_INT = 0.004/12.0 #https://www.bankrate.com/finance/interest-rates-forecast/
Investor.PAR_TAX_DEF_DEPOSIT_CAP = 19500 #per year https://www.irs.gov/retirement-plans/plan-participant-employee/retirement-topics-contributions
Investor.PAR_TAX_DEF_COMPANY_MATCH = 1.08 #company match
try:
returns
except NameError:
returns = []
print("... initialized returns global variable")
try:
losses
except NameError:
losses = []
print("... initialized losses global variable")
num_iterations = 5000
# num_iterations = 5000
log_interval = 100
eval_interval = 5000
num_eval_episodes = 100
print(f"Training: This run has {num_iterations:,} steps: from step {tf_agent.train_step_counter.numpy():,} to step {tf_agent.train_step_counter.numpy() + num_iterations:,} ...")
# tf_agent.train_step_counter.assign(0) # Reset the train step
# Optimize by wrapping some of the code in a graph using TF function (Optional):
tf_agent.train = common.function(tf_agent.train)
# Eval agent's policy once before NEW training:
if len(losses) == 0:
avg_return = compute_avg_return(eval_env, tf_agent.policy, num_eval_episodes) #comment out to save time with short trial runs
returns.append(avg_return)# returns = [] #returns = [avg_return]
for i in range(num_iterations):
# Collect a few steps using collect_policy and save to the replay buffer
collect_driver.run()
# Sample a batch of data from the buffer and update the agent's network
experience, unused_info = next(iterator)
train_loss = tf_agent.train(experience).loss
losses.append(train_loss.numpy())
step = tf_agent.train_step_counter.numpy(); #print(f'i={i}, step={step}: loss={train_loss}')
# step,train_loss = train_one_iteration(); #print(f'i={i}, step={step}: loss={train_loss}')
if step % log_interval == 0: print(f'i={i+1}, step={step}: loss={train_loss}')
if step % eval_interval == 0:
avg_return = compute_avg_return(eval_env, tf_agent.policy, num_eval_episodes)
print(f'step={step}: Average Return={avg_return}')
returns.append(avg_return)
print('returns:', returns)
with open(returns_pkl_file, "wb") as fp: pickle.dump(returns, fp)
with open(losses_pkl_file, "wb") as fp: pickle.dump(losses, fp)
train_checkpointer.save(global_step)
tf_policy_saver.save(str(policy_dir))
Training: This run has 5,000 steps: from step 45,000 to step 50,000 ... i=100, step=45100: loss=0.0017131200293079019 i=200, step=45200: loss=0.0014729814138263464 i=300, step=45300: loss=0.0012242922093719244 i=400, step=45400: loss=0.0015161782503128052 i=500, step=45500: loss=0.001114063779823482 i=600, step=45600: loss=0.0016814216505736113 i=700, step=45700: loss=0.0011397635098546743 i=800, step=45800: loss=0.0013222007546573877 i=900, step=45900: loss=0.00154818722512573 i=1000, step=46000: loss=0.0013686884194612503 i=1100, step=46100: loss=0.0013849932001903653 i=1200, step=46200: loss=0.0013291839277371764 i=1300, step=46300: loss=0.0012582483468577266 i=1400, step=46400: loss=0.0013634013012051582 i=1500, step=46500: loss=0.0013876595767214894 i=1600, step=46600: loss=0.0014104037545621395 i=1700, step=46700: loss=0.001459364895708859 i=1800, step=46800: loss=0.0016233036294579506 i=1900, step=46900: loss=0.0015679430216550827 i=2000, step=47000: loss=0.0016755771357566118 i=2100, step=47100: loss=0.0017665171762928367 i=2200, step=47200: loss=0.0017057405784726143 i=2300, step=47300: loss=0.0015854313969612122 i=2400, step=47400: loss=0.001479185651987791 i=2500, step=47500: loss=0.0014413316966965795 i=2600, step=47600: loss=0.0014852848835289478 i=2700, step=47700: loss=0.002051839604973793 i=2800, step=47800: loss=0.0017257879953831434 i=2900, step=47900: loss=0.0017526663141325116 i=3000, step=48000: loss=0.0015138584421947598 i=3100, step=48100: loss=0.0015865201130509377 i=3200, step=48200: loss=0.001458585262298584 i=3300, step=48300: loss=0.001544468803331256 i=3400, step=48400: loss=0.001621721894480288 i=3500, step=48500: loss=0.0013865592190995812 i=3600, step=48600: loss=0.0020001097582280636 i=3700, step=48700: loss=0.0015221710782498121 i=3800, step=48800: loss=0.0019307738402858377 i=3900, step=48900: loss=0.001414790516719222 i=4000, step=49000: loss=0.0015523519832640886 i=4100, step=49100: loss=0.0013826192589476705 i=4200, step=49200: loss=0.001234834548085928 i=4300, step=49300: loss=0.0012455774703994393 i=4400, step=49400: loss=0.0012584668584167957 i=4500, step=49500: loss=0.0014137454563751817 i=4600, step=49600: loss=0.0016291745705530047 i=4700, step=49700: loss=0.001165154855698347 i=4800, step=49800: loss=0.001339361653663218 i=4900, step=49900: loss=0.0013172781327739358 i=5000, step=50000: loss=0.0020026033744215965 ... computing avg return with num_episodes=100 step=50000: Average Return=0.9651120901107788 returns: [0.91741705, 1.0443436, 0.5601131, 0.5663353, 0.8625177, 0.8818096, 0.8853596, 0.8716588, 0.9818827, 0.86259454, 0.9651121]
WARNING:absl:Found untraced functions such as ActorNetwork_layer_call_and_return_conditional_losses, ActorNetwork_layer_call_fn, ActorNetwork_layer_call_fn, ActorNetwork_layer_call_and_return_conditional_losses, ActorNetwork_layer_call_and_return_conditional_losses while saving (showing 5 of 25). These functions will not be directly callable after loading. WARNING:absl:Found untraced functions such as ActorNetwork_layer_call_and_return_conditional_losses, ActorNetwork_layer_call_fn, ActorNetwork_layer_call_fn, ActorNetwork_layer_call_and_return_conditional_losses, ActorNetwork_layer_call_and_return_conditional_losses while saving (showing 5 of 25). These functions will not be directly callable after loading.
INFO:tensorflow:Assets written to: /content/gdrive/My Drive/RL/TF-Agents/InvestorProject/policy7/policy/assets
INFO:tensorflow:Assets written to: /content/gdrive/My Drive/RL/TF-Agents/InvestorProject/policy7/policy/assets
print(global_step)
<tf.Variable 'global_step:0' shape=() dtype=int64, numpy=50000>
evals = range(0, global_step.numpy()+1, eval_interval); print(f"evals: {evals}")
list(evals)
evals: range(0, 50001, 5000)
[0, 5000, 10000, 15000, 20000, 25000, 30000, 35000, 40000, 45000, 50000]
print(eval_interval)
# evals = range(0, num_iterations+1, eval_interval); print(f"evals: {evals}, \nlist(evals): {list(evals)}, \nreturns: {returns}")
evals = range(0, global_step.numpy()+1, eval_interval); print(f"evals: {evals}, \nlist(evals): {list(evals)}, \nreturns: {returns}")
plt.plot(list(evals), returns)
plt.scatter(list(evals), returns)
plt.xlabel('Iterations')
plt.ylabel('Average Return')
# plt.ylim(top=50)
5000 evals: range(0, 50001, 5000), list(evals): [0, 5000, 10000, 15000, 20000, 25000, 30000, 35000, 40000, 45000, 50000], returns: [0.91741705, 1.0443436, 0.5601131, 0.5663353, 0.8625177, 0.8818096, 0.8853596, 0.8716588, 0.9818827, 0.86259454, 0.9651121]
Text(0, 0.5, 'Average Return')
its = range(0, global_step.numpy()); print(f"its: {its}, \nlist(its): {list(its)}, \nlosses: {losses}")
plt.plot(losses)
# plt.plot([e.numpy() for e in losses])
# plt.scatter(list(its), losses)
plt.xlabel('Iterations')
plt.ylabel('Loss')
its: range(0, 50000), list(its): [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 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38865, 38866, 38867, 38868, 38869, 38870, 38871, 38872, 38873, 38874, 38875, 38876, 38877, 38878, 38879, 38880, 38881, 38882, 38883, 38884, 38885, 38886, 38887, 38888, 38889, 38890, 38891, 38892, 38893, 38894, 38895, 38896, 38897, 38898, 38899, 38900, 38901, 38902, 38903, 38904, 38905, 38906, 38907, 38908, 38909, 38910, 38911, 38912, 38913, 38914, 38915, 38916, 38917, 38918, 38919, 38920, 38921, 38922, 38923, 38924, 38925, 38926, 38927, 38928, 38929, 38930, 38931, 38932, 38933, 38934, 38935, 38936, 38937, 38938, 38939, 38940, 38941, 38942, 38943, 38944, 38945, 38946, 38947, 38948, 38949, 38950, 38951, 38952, 38953, 38954, 38955, 38956, 38957, 38958, 38959, 38960, 38961, 38962, 38963, 38964, 38965, 38966, 38967, 38968, 38969, 38970, 38971, 38972, 38973, 38974, 38975, 38976, 38977, 38978, 38979, 38980, 38981, 38982, 38983, 38984, 38985, 38986, 38987, 38988, 38989, 38990, 38991, 38992, 38993, 38994, 38995, 38996, 38997, 38998, 38999, 39000, 39001, 39002, 39003, 39004, 39005, 39006, 39007, 39008, 39009, 39010, 39011, 39012, 39013, 39014, 39015, 39016, 39017, 39018, 39019, 39020, 39021, 39022, 39023, 39024, 39025, 39026, 39027, 39028, 39029, 39030, 39031, 39032, 39033, 39034, 39035, 39036, 39037, 39038, 39039, 39040, 39041, 39042, 39043, 39044, 39045, 39046, 39047, 39048, 39049, 39050, 39051, 39052, 39053, 39054, 39055, 39056, 39057, 39058, 39059, 39060, 39061, 39062, 39063, 39064, 39065, 39066, 39067, 39068, 39069, 39070, 39071, 39072, 39073, 39074, 39075, 39076, 39077, 39078, 39079, 39080, 39081, 39082, 39083, 39084, 39085, 39086, 39087, 39088, 39089, 39090, 39091, 39092, 39093, 39094, 39095, 39096, 39097, 39098, 39099, 39100, 39101, 39102, 39103, 39104, 39105, 39106, 39107, 39108, 39109, 39110, 39111, 39112, 39113, 39114, 39115, 39116, 39117, 39118, 39119, 39120, 39121, 39122, 39123, 39124, 39125, 39126, 39127, 39128, 39129, 39130, 39131, 39132, 39133, 39134, 39135, 39136, 39137, 39138, 39139, 39140, 39141, 39142, 39143, 39144, 39145, 39146, 39147, 39148, 39149, 39150, 39151, 39152, 39153, 39154, 39155, 39156, 39157, 39158, 39159, 39160, 39161, 39162, 39163, 39164, 39165, 39166, 39167, 39168, 39169, 39170, 39171, 39172, 39173, 39174, 39175, 39176, 39177, 39178, 39179, 39180, 39181, 39182, 39183, 39184, 39185, 39186, 39187, 39188, 39189, 39190, 39191, 39192, 39193, 39194, 39195, 39196, 39197, 39198, 39199, 39200, 39201, 39202, 39203, 39204, 39205, 39206, 39207, 39208, 39209, 39210, 39211, 39212, 39213, 39214, 39215, 39216, 39217, 39218, 39219, 39220, 39221, 39222, 39223, 39224, 39225, 39226, 39227, 39228, 39229, 39230, 39231, 39232, 39233, 39234, 39235, 39236, 39237, 39238, 39239, 39240, 39241, 39242, 39243, 39244, 39245, 39246, 39247, 39248, 39249, 39250, 39251, 39252, 39253, 39254, 39255, 39256, 39257, 39258, 39259, 39260, 39261, 39262, 39263, 39264, 39265, 39266, 39267, 39268, 39269, 39270, 39271, 39272, 39273, 39274, 39275, 39276, 39277, 39278, 39279, 39280, 39281, 39282, 39283, 39284, 39285, 39286, 39287, 39288, 39289, 39290, 39291, 39292, 39293, 39294, 39295, 39296, 39297, 39298, 39299, 39300, 39301, 39302, 39303, 39304, 39305, 39306, 39307, 39308, 39309, 39310, 39311, 39312, 39313, 39314, 39315, 39316, 39317, 39318, 39319, 39320, 39321, 39322, 39323, 39324, 39325, 39326, 39327, 39328, 39329, 39330, 39331, 39332, 39333, 39334, 39335, 39336, 39337, 39338, 39339, 39340, 39341, 39342, 39343, 39344, 39345, 39346, 39347, 39348, 39349, 39350, 39351, 39352, 39353, 39354, 39355, 39356, 39357, 39358, 39359, 39360, 39361, 39362, 39363, 39364, 39365, 39366, 39367, 39368, 39369, 39370, 39371, 39372, 39373, 39374, 39375, 39376, 39377, 39378, 39379, 39380, 39381, 39382, 39383, 39384, 39385, 39386, 39387, 39388, 39389, 39390, 39391, 39392, 39393, 39394, 39395, 39396, 39397, 39398, 39399, 39400, 39401, 39402, 39403, 39404, 39405, 39406, 39407, 39408, 39409, 39410, 39411, 39412, 39413, 39414, 39415, 39416, 39417, 39418, 39419, 39420, 39421, 39422, 39423, 39424, 39425, 39426, 39427, 39428, 39429, 39430, 39431, 39432, 39433, 39434, 39435, 39436, 39437, 39438, 39439, 39440, 39441, 39442, 39443, 39444, 39445, 39446, 39447, 39448, 39449, 39450, 39451, 39452, 39453, 39454, 39455, 39456, 39457, 39458, 39459, 39460, 39461, 39462, 39463, 39464, 39465, 39466, 39467, 39468, 39469, 39470, 39471, 39472, 39473, 39474, 39475, 39476, 39477, 39478, 39479, 39480, 39481, 39482, 39483, 39484, 39485, 39486, 39487, 39488, 39489, 39490, 39491, 39492, 39493, 39494, 39495, 39496, 39497, 39498, 39499, 39500, 39501, 39502, 39503, 39504, 39505, 39506, 39507, 39508, 39509, 39510, 39511, 39512, 39513, 39514, 39515, 39516, 39517, 39518, 39519, 39520, 39521, 39522, 39523, 39524, 39525, 39526, 39527, 39528, 39529, 39530, 39531, 39532, 39533, 39534, 39535, 39536, 39537, 39538, 39539, 39540, 39541, 39542, 39543, 39544, 39545, 39546, 39547, 39548, 39549, 39550, 39551, 39552, 39553, 39554, 39555, 39556, 39557, 39558, 39559, 39560, 39561, 39562, 39563, 39564, 39565, 39566, 39567, 39568, 39569, 39570, 39571, 39572, 39573, 39574, 39575, 39576, 39577, 39578, 39579, 39580, 39581, 39582, 39583, 39584, 39585, 39586, 39587, 39588, 39589, 39590, 39591, 39592, 39593, 39594, 39595, 39596, 39597, 39598, 39599, 39600, 39601, 39602, 39603, 39604, 39605, 39606, 39607, 39608, 39609, 39610, 39611, 39612, 39613, 39614, 39615, 39616, 39617, 39618, 39619, 39620, 39621, 39622, 39623, 39624, 39625, 39626, 39627, 39628, 39629, 39630, 39631, 39632, 39633, 39634, 39635, 39636, 39637, 39638, 39639, 39640, 39641, 39642, 39643, 39644, 39645, 39646, 39647, 39648, 39649, 39650, 39651, 39652, 39653, 39654, 39655, 39656, 39657, 39658, 39659, 39660, 39661, 39662, 39663, 39664, 39665, 39666, 39667, 39668, 39669, 39670, 39671, 39672, 39673, 39674, 39675, 39676, 39677, 39678, 39679, 39680, 39681, 39682, 39683, 39684, 39685, 39686, 39687, 39688, 39689, 39690, 39691, 39692, 39693, 39694, 39695, 39696, 39697, 39698, 39699, 39700, 39701, 39702, 39703, 39704, 39705, 39706, 39707, 39708, 39709, 39710, 39711, 39712, 39713, 39714, 39715, 39716, 39717, 39718, 39719, 39720, 39721, 39722, 39723, 39724, 39725, 39726, 39727, 39728, 39729, 39730, 39731, 39732, 39733, 39734, 39735, 39736, 39737, 39738, 39739, 39740, 39741, 39742, 39743, 39744, 39745, 39746, 39747, 39748, 39749, 39750, 39751, 39752, 39753, 39754, 39755, 39756, 39757, 39758, 39759, 39760, 39761, 39762, 39763, 39764, 39765, 39766, 39767, 39768, 39769, 39770, 39771, 39772, 39773, 39774, 39775, 39776, 39777, 39778, 39779, 39780, 39781, 39782, 39783, 39784, 39785, 39786, 39787, 39788, 39789, 39790, 39791, 39792, 39793, 39794, 39795, 39796, 39797, 39798, 39799, 39800, 39801, 39802, 39803, 39804, 39805, 39806, 39807, 39808, 39809, 39810, 39811, 39812, 39813, 39814, 39815, 39816, 39817, 39818, 39819, 39820, 39821, 39822, 39823, 39824, 39825, 39826, 39827, 39828, 39829, 39830, 39831, 39832, 39833, 39834, 39835, 39836, 39837, 39838, 39839, 39840, 39841, 39842, 39843, 39844, 39845, 39846, 39847, 39848, 39849, 39850, 39851, 39852, 39853, 39854, 39855, 39856, 39857, 39858, 39859, 39860, 39861, 39862, 39863, 39864, 39865, 39866, 39867, 39868, 39869, 39870, 39871, 39872, 39873, 39874, 39875, 39876, 39877, 39878, 39879, 39880, 39881, 39882, 39883, 39884, 39885, 39886, 39887, 39888, 39889, 39890, 39891, 39892, 39893, 39894, 39895, 39896, 39897, 39898, 39899, 39900, 39901, 39902, 39903, 39904, 39905, 39906, 39907, 39908, 39909, 39910, 39911, 39912, 39913, 39914, 39915, 39916, 39917, 39918, 39919, 39920, 39921, 39922, 39923, 39924, 39925, 39926, 39927, 39928, 39929, 39930, 39931, 39932, 39933, 39934, 39935, 39936, 39937, 39938, 39939, 39940, 39941, 39942, 39943, 39944, 39945, 39946, 39947, 39948, 39949, 39950, 39951, 39952, 39953, 39954, 39955, 39956, 39957, 39958, 39959, 39960, 39961, 39962, 39963, 39964, 39965, 39966, 39967, 39968, 39969, 39970, 39971, 39972, 39973, 39974, 39975, 39976, 39977, 39978, 39979, 39980, 39981, 39982, 39983, 39984, 39985, 39986, 39987, 39988, 39989, 39990, 39991, 39992, 39993, 39994, 39995, 39996, 39997, 39998, 39999, 40000, 40001, 40002, 40003, 40004, 40005, 40006, 40007, 40008, 40009, 40010, 40011, 40012, 40013, 40014, 40015, 40016, 40017, 40018, 40019, 40020, 40021, 40022, 40023, 40024, 40025, 40026, 40027, 40028, 40029, 40030, 40031, 40032, 40033, 40034, 40035, 40036, 40037, 40038, 40039, 40040, 40041, 40042, 40043, 40044, 40045, 40046, 40047, 40048, 40049, 40050, 40051, 40052, 40053, 40054, 40055, 40056, 40057, 40058, 40059, 40060, 40061, 40062, 40063, 40064, 40065, 40066, 40067, 40068, 40069, 40070, 40071, 40072, 40073, 40074, 40075, 40076, 40077, 40078, 40079, 40080, 40081, 40082, 40083, 40084, 40085, 40086, 40087, 40088, 40089, 40090, 40091, 40092, 40093, 40094, 40095, 40096, 40097, 40098, 40099, 40100, 40101, 40102, 40103, 40104, 40105, 40106, 40107, 40108, 40109, 40110, 40111, 40112, 40113, 40114, 40115, 40116, 40117, 40118, 40119, 40120, 40121, 40122, 40123, 40124, 40125, 40126, 40127, 40128, 40129, 40130, 40131, 40132, 40133, 40134, 40135, 40136, 40137, 40138, 40139, 40140, 40141, 40142, 40143, 40144, 40145, 40146, 40147, 40148, 40149, 40150, 40151, 40152, 40153, 40154, 40155, 40156, 40157, 40158, 40159, 40160, 40161, 40162, 40163, 40164, 40165, 40166, 40167, 40168, 40169, 40170, 40171, 40172, 40173, 40174, 40175, 40176, 40177, 40178, 40179, 40180, 40181, 40182, 40183, 40184, 40185, 40186, 40187, 40188, 40189, 40190, 40191, 40192, 40193, 40194, 40195, 40196, 40197, 40198, 40199, 40200, 40201, 40202, 40203, 40204, 40205, 40206, 40207, 40208, 40209, 40210, 40211, 40212, 40213, 40214, 40215, 40216, 40217, 40218, 40219, 40220, 40221, 40222, 40223, 40224, 40225, 40226, 40227, 40228, 40229, 40230, 40231, 40232, 40233, 40234, 40235, 40236, 40237, 40238, 40239, 40240, 40241, 40242, 40243, 40244, 40245, 40246, 40247, 40248, 40249, 40250, 40251, 40252, 40253, 40254, 40255, 40256, 40257, 40258, 40259, 40260, 40261, 40262, 40263, 40264, 40265, 40266, 40267, 40268, 40269, 40270, 40271, 40272, 40273, 40274, 40275, 40276, 40277, 40278, 40279, 40280, 40281, 40282, 40283, 40284, 40285, 40286, 40287, 40288, 40289, 40290, 40291, 40292, 40293, 40294, 40295, 40296, 40297, 40298, 40299, 40300, 40301, 40302, 40303, 40304, 40305, 40306, 40307, 40308, 40309, 40310, 40311, 40312, 40313, 40314, 40315, 40316, 40317, 40318, 40319, 40320, 40321, 40322, 40323, 40324, 40325, 40326, 40327, 40328, 40329, 40330, 40331, 40332, 40333, 40334, 40335, 40336, 40337, 40338, 40339, 40340, 40341, 40342, 40343, 40344, 40345, 40346, 40347, 40348, 40349, 40350, 40351, 40352, 40353, 40354, 40355, 40356, 40357, 40358, 40359, 40360, 40361, 40362, 40363, 40364, 40365, 40366, 40367, 40368, 40369, 40370, 40371, 40372, 40373, 40374, 40375, 40376, 40377, 40378, 40379, 40380, 40381, 40382, 40383, 40384, 40385, 40386, 40387, 40388, 40389, 40390, 40391, 40392, 40393, 40394, 40395, 40396, 40397, 40398, 40399, 40400, 40401, 40402, 40403, 40404, 40405, 40406, 40407, 40408, 40409, 40410, 40411, 40412, 40413, 40414, 40415, 40416, 40417, 40418, 40419, 40420, 40421, 40422, 40423, 40424, 40425, 40426, 40427, 40428, 40429, 40430, 40431, 40432, 40433, 40434, 40435, 40436, 40437, 40438, 40439, 40440, 40441, 40442, 40443, 40444, 40445, 40446, 40447, 40448, 40449, 40450, 40451, 40452, 40453, 40454, 40455, 40456, 40457, 40458, 40459, 40460, 40461, 40462, 40463, 40464, 40465, 40466, 40467, 40468, 40469, 40470, 40471, 40472, 40473, 40474, 40475, 40476, 40477, 40478, 40479, 40480, 40481, 40482, 40483, 40484, 40485, 40486, 40487, 40488, 40489, 40490, 40491, 40492, 40493, 40494, 40495, 40496, 40497, 40498, 40499, 40500, 40501, 40502, 40503, 40504, 40505, 40506, 40507, 40508, 40509, 40510, 40511, 40512, 40513, 40514, 40515, 40516, 40517, 40518, 40519, 40520, 40521, 40522, 40523, 40524, 40525, 40526, 40527, 40528, 40529, 40530, 40531, 40532, 40533, 40534, 40535, 40536, 40537, 40538, 40539, 40540, 40541, 40542, 40543, 40544, 40545, 40546, 40547, 40548, 40549, 40550, 40551, 40552, 40553, 40554, 40555, 40556, 40557, 40558, 40559, 40560, 40561, 40562, 40563, 40564, 40565, 40566, 40567, 40568, 40569, 40570, 40571, 40572, 40573, 40574, 40575, 40576, 40577, 40578, 40579, 40580, 40581, 40582, 40583, 40584, 40585, 40586, 40587, 40588, 40589, 40590, 40591, 40592, 40593, 40594, 40595, 40596, 40597, 40598, 40599, 40600, 40601, 40602, 40603, 40604, 40605, 40606, 40607, 40608, 40609, 40610, 40611, 40612, 40613, 40614, 40615, 40616, 40617, 40618, 40619, 40620, 40621, 40622, 40623, 40624, 40625, 40626, 40627, 40628, 40629, 40630, 40631, 40632, 40633, 40634, 40635, 40636, 40637, 40638, 40639, 40640, 40641, 40642, 40643, 40644, 40645, 40646, 40647, 40648, 40649, 40650, 40651, 40652, 40653, 40654, 40655, 40656, 40657, 40658, 40659, 40660, 40661, 40662, 40663, 40664, 40665, 40666, 40667, 40668, 40669, 40670, 40671, 40672, 40673, 40674, 40675, 40676, 40677, 40678, 40679, 40680, 40681, 40682, 40683, 40684, 40685, 40686, 40687, 40688, 40689, 40690, 40691, 40692, 40693, 40694, 40695, 40696, 40697, 40698, 40699, 40700, 40701, 40702, 40703, 40704, 40705, 40706, 40707, 40708, 40709, 40710, 40711, 40712, 40713, 40714, 40715, 40716, 40717, 40718, 40719, 40720, 40721, 40722, 40723, 40724, 40725, 40726, 40727, 40728, 40729, 40730, 40731, 40732, 40733, 40734, 40735, 40736, 40737, 40738, 40739, 40740, 40741, 40742, 40743, 40744, 40745, 40746, 40747, 40748, 40749, 40750, 40751, 40752, 40753, 40754, 40755, 40756, 40757, 40758, 40759, 40760, 40761, 40762, 40763, 40764, 40765, 40766, 40767, 40768, 40769, 40770, 40771, 40772, 40773, 40774, 40775, 40776, 40777, 40778, 40779, 40780, 40781, 40782, 40783, 40784, 40785, 40786, 40787, 40788, 40789, 40790, 40791, 40792, 40793, 40794, 40795, 40796, 40797, 40798, 40799, 40800, 40801, 40802, 40803, 40804, 40805, 40806, 40807, 40808, 40809, 40810, 40811, 40812, 40813, 40814, 40815, 40816, 40817, 40818, 40819, 40820, 40821, 40822, 40823, 40824, 40825, 40826, 40827, 40828, 40829, 40830, 40831, 40832, 40833, 40834, 40835, 40836, 40837, 40838, 40839, 40840, 40841, 40842, 40843, 40844, 40845, 40846, 40847, 40848, 40849, 40850, 40851, 40852, 40853, 40854, 40855, 40856, 40857, 40858, 40859, 40860, 40861, 40862, 40863, 40864, 40865, 40866, 40867, 40868, 40869, 40870, 40871, 40872, 40873, 40874, 40875, 40876, 40877, 40878, 40879, 40880, 40881, 40882, 40883, 40884, 40885, 40886, 40887, 40888, 40889, 40890, 40891, 40892, 40893, 40894, 40895, 40896, 40897, 40898, 40899, 40900, 40901, 40902, 40903, 40904, 40905, 40906, 40907, 40908, 40909, 40910, 40911, 40912, 40913, 40914, 40915, 40916, 40917, 40918, 40919, 40920, 40921, 40922, 40923, 40924, 40925, 40926, 40927, 40928, 40929, 40930, 40931, 40932, 40933, 40934, 40935, 40936, 40937, 40938, 40939, 40940, 40941, 40942, 40943, 40944, 40945, 40946, 40947, 40948, 40949, 40950, 40951, 40952, 40953, 40954, 40955, 40956, 40957, 40958, 40959, 40960, 40961, 40962, 40963, 40964, 40965, 40966, 40967, 40968, 40969, 40970, 40971, 40972, 40973, 40974, 40975, 40976, 40977, 40978, 40979, 40980, 40981, 40982, 40983, 40984, 40985, 40986, 40987, 40988, 40989, 40990, 40991, 40992, 40993, 40994, 40995, 40996, 40997, 40998, 40999, 41000, 41001, 41002, 41003, 41004, 41005, 41006, 41007, 41008, 41009, 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41153, 41154, 41155, 41156, 41157, 41158, 41159, 41160, 41161, 41162, 41163, 41164, 41165, 41166, 41167, 41168, 41169, 41170, 41171, 41172, 41173, 41174, 41175, 41176, 41177, 41178, 41179, 41180, 41181, 41182, 41183, 41184, 41185, 41186, 41187, 41188, 41189, 41190, 41191, 41192, 41193, 41194, 41195, 41196, 41197, 41198, 41199, 41200, 41201, 41202, 41203, 41204, 41205, 41206, 41207, 41208, 41209, 41210, 41211, 41212, 41213, 41214, 41215, 41216, 41217, 41218, 41219, 41220, 41221, 41222, 41223, 41224, 41225, 41226, 41227, 41228, 41229, 41230, 41231, 41232, 41233, 41234, 41235, 41236, 41237, 41238, 41239, 41240, 41241, 41242, 41243, 41244, 41245, 41246, 41247, 41248, 41249, 41250, 41251, 41252, 41253, 41254, 41255, 41256, 41257, 41258, 41259, 41260, 41261, 41262, 41263, 41264, 41265, 41266, 41267, 41268, 41269, 41270, 41271, 41272, 41273, 41274, 41275, 41276, 41277, 41278, 41279, 41280, 41281, 41282, 41283, 41284, 41285, 41286, 41287, 41288, 41289, 41290, 41291, 41292, 41293, 41294, 41295, 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Text(0, 0.5, 'Loss')
# Save returns
with open(returns_pkl_file, "wb") as fp: pickle.dump(returns, fp)
# Save losses
with open(losses_pkl_file, "wb") as fp: pickle.dump(losses, fp)
# Save checkpoint
train_checkpointer.save(global_step)
!ls -l "{checkpoint_dir}"
total 42235 -rw------- 1 root root 448 Feb 2 17:33 checkpoint -rw------- 1 root root 10806404 Feb 2 16:11 ckpt-10000.data-00000-of-00001 -rw------- 1 root root 4740 Feb 2 16:11 ckpt-10000.index -rw------- 1 root root 10806404 Feb 2 16:52 ckpt-15000.data-00000-of-00001 -rw------- 1 root root 4740 Feb 2 16:52 ckpt-15000.index -rw------- 1 root root 10806404 Feb 2 17:33 ckpt-20000.data-00000-of-00001 -rw------- 1 root root 4740 Feb 2 17:33 ckpt-20000.index -rw------- 1 root root 10806404 Feb 2 15:03 ckpt-5000.data-00000-of-00001 -rw------- 1 root root 4740 Feb 2 15:03 ckpt-5000.index
# Save policy
tf_policy_saver.save(str(policy_dir))
!ls -l "{policy_dir}"
WARNING:absl:Found untraced functions such as ActorNetwork_layer_call_fn, ActorNetwork_layer_call_and_return_conditional_losses, ActorNetwork_layer_call_fn, ActorNetwork_layer_call_and_return_conditional_losses, ActorNetwork_layer_call_and_return_conditional_losses while saving (showing 5 of 25). These functions will not be directly callable after loading. WARNING:absl:Found untraced functions such as ActorNetwork_layer_call_fn, ActorNetwork_layer_call_and_return_conditional_losses, ActorNetwork_layer_call_fn, ActorNetwork_layer_call_and_return_conditional_losses, ActorNetwork_layer_call_and_return_conditional_losses while saving (showing 5 of 25). These functions will not be directly callable after loading.
INFO:tensorflow:Assets written to: /content/gdrive/My Drive/RL/TF-Agents/InvestorProject/policy5/policy/assets
INFO:tensorflow:Assets written to: /content/gdrive/My Drive/RL/TF-Agents/InvestorProject/policy5/policy/assets
total 91 drwx------ 2 root root 4096 Feb 2 15:03 assets -rw------- 1 root root 2798 Feb 2 17:33 policy_specs.pbtxt -rw------- 1 root root 81158 Feb 2 17:33 saved_model.pb drwx------ 2 root root 4096 Feb 2 17:33 variables
def eval_policy(pol, num_episodes=5):
actions = np.array([0,0,0,0])
observations = [0,0,0,0,0,0,0]
rewards = [0]
expenses = [0]
home_actual_pmts = [0]
deposit_taxables = [0]
deposit_tax_defs = [0]
luxury_spends = [0]
networths = [0]
for i in range(num_episodes):
print(f"episode {i}")
tstep = eval_env.reset()
s = 0
while not tstep.is_last():
# if s == 100: break
#print(f"step {s}:")
pstep = pol.action(tstep); #tf.print(f"{pstep.action[0]}")
action = np.array(pstep.action.numpy()[0]); #tf.print(f"action={action}")
actions = np.vstack([actions, action])
tstep = eval_env.step(pstep.action); #tf.print(tstep.observation[0])
observation = np.array(tstep.observation.numpy()[0])
observations = np.vstack([observations, observation])
reward = np.array(tstep.reward.numpy()[0])
rewards = np.vstack([rewards, reward])
ke_expense,ke_home_actual_pmt,ke_deposit_taxable,ke_deposit_tax_def,ke_luxury_spend,ke_networth = eval_py_env.render() #.
expenses = np.vstack([expenses, ke_expense])
home_actual_pmts = np.vstack([home_actual_pmts, ke_home_actual_pmt])
deposit_taxables = np.vstack([deposit_taxables, ke_deposit_taxable])
deposit_tax_defs = np.vstack([deposit_tax_defs, ke_deposit_tax_def])
luxury_spends = np.vstack([luxury_spends, ke_luxury_spend])
networths = np.vstack([networths, ke_networth])
s += 1
return actions,observations,rewards, expenses,home_actual_pmts,deposit_taxables,deposit_tax_defs,luxury_spends,networths
We now show how an experimentor may use our model:
eval_py_env.gym.verbose = False
Investor.PAR_START_AGE = 32
Investor.PAR_RETIRE_AGE = 65
Investor.PAR_INFLATION = 0.014/12.0 #for 12 months ended December 2020; https://www.usinflationcalculator.com/inflation/current-inflation-rates/
Investor.PAR_TAX_RATE = 0.22/12.0 #https://www.businessinsider.com/personal-finance/what-tax-bracket-am-i-in-federal-income-tax
Investor.PAR_SALARY_LOW = 125000; Investor.PAR_SALARY_HIGH = 125001
Investor.PAR_EXPENSES = 0.51 #of salary
house_multiple = 140000/Investor.PAR_SALARY_LOW; print(f"house_multiple: {house_multiple}")
Investor.PAR_HOUSE_MULT_LOW = house_multiple; Investor.PAR_HOUSE_MULT_HIGH = house_multiple
Investor.PAR_MORT_INT = 0.03/12.0 #https://www.bankrate.com/finance/interest-rates-forecast/
Investor.PAR_INVEST_INT = 0.004/12.0 #https://www.bankrate.com/finance/interest-rates-forecast/
Investor.PAR_TAX_DEF_DEPOSIT_CAP = 19500 #per year https://www.irs.gov/retirement-plans/plan-participant-employee/retirement-topics-contributions
Investor.PAR_TAX_DEF_COMPANY_MATCH = 1.08 #company match
house_multiple: 1.12
# POINT TO CHECKPOINT OF POLICY WE WANT TO USE
# tf_agent.train_step_counter.assign(50000)
# global_step = tf.compat.v1.train.get_global_step()
# tf.print(global_step)
# Restore policy
# The policy can be loaded without having any knowledge of what agent or
# network was used to create it. This makes deployment of the policy easier.
saved_policy = tf.compat.v2.saved_model.load(str(policy_dir))
# EVALUATION
# actions,observations,rewards,expenses,home_actual_pmts,deposit_taxables,deposit_tax_defs,networths = eval_policy(tf_agent.policy, 5)
actions,observations,rewards,expenses,home_actual_pmts,deposit_taxables,deposit_tax_defs,luxury_spends,networths = eval_policy(saved_policy, 1)
episode 0
num_t = len(actions[:,0]); print('Number of months:', num_t) #number of ts
print(f"Networth at age {Investor.PAR_RETIRE_AGE}: ${networths[-1][0]:,.0f}")
Number of months: 398 Networth at age 65: $3,181,499
mpl.rcParams['lines.linewidth'] = 1.2
t = np.arange(0, num_t); fig,(axs0,axs1,axs2,axs3,axs4) = plt.subplots(5, sharex=True)
fig.set_figwidth(25); fig.set_figheight(10)
axs0.set_ylabel('Dollars');axs1.set_ylabel('Dollars');axs2.set_ylabel('Dollars');axs3.set_ylabel('Dollars')
axs0.set_xlim(0, len(actions[:,0]));
axs0.set_ylim(auto=True); axs0.spines['top'].set_visible(False); axs0.spines['right'].set_visible(True); axs0.spines['bottom'].set_visible(False)
axs0.plot(t, observations[:,4]*Investor.MEG, 'r--', label='STA_HOME_REQ_PAYMENT')
axs0.plot(t, home_actual_pmts[:,0], 'r-', label='home_actual_pmt')
axs0.plot(t, deposit_tax_defs[:,0], 'g-', label='deposit_tax_def')
axs0.plot(t, deposit_taxables[:,0], '-', color='darkorange', label='deposit_taxable')
axs0.plot(t, luxury_spends[:,0], '-', color='magenta', label='luxury_spend')
axs1.set_ylim(auto=True); axs1.spines['top'].set_visible(False); axs1.spines['right'].set_visible(True); axs1.spines['bottom'].set_visible(False)
axs1.plot(t, observations[:,1]*Investor.MEG, 'b-', label='STA_SALARY')
axs1.plot(t, expenses[:,0], 'y--', label='expenses')
axs2.set_ylim(auto=True); axs2.spines['top'].set_visible(False); axs2.spines['right'].set_visible(True); axs2.spines['bottom'].set_visible(False)
axs2.plot(t, observations[:,2]*Investor.MEG, 'r.', label='STA_HOME_VALUE')
axs2.plot(t, observations[:,3]*Investor.MEG, 'r-', label='STA_HOME_LOAN')
axs3.set_ylim(auto=True); axs3.spines['top'].set_visible(False); axs3.spines['right'].set_visible(True); axs3.spines['bottom'].set_visible(True)
axs3.plot(t, observations[:,5]*Investor.MEG, 'g-', label='STA_SAVE_TAX_DEF')
axs3.plot(t, observations[:,6]*Investor.MEG, '-', color='darkorange', label='STA_SAVE_TAXABLE')
axs3.plot(t, networths[:,0], 'k.', label='net_worth')
axs4.set_ylim(auto=True); axs4.spines['top'].set_visible(False); axs4.spines['right'].set_visible(True); axs4.spines['bottom'].set_visible(True)
axs4.plot(t, rewards[:,0]*Investor.MEG, 'k--', label='REWARD')
axs4.set_xlabel('STEPS (Months)')
axs0.legend(); axs1.legend(); axs2.legend(); axs3.legend(); axs4.legend()
axs_years = axs0.twiny()
years_tick_locations = np.arange(0, num_t, 12)
axs_years.set_xticks(years_tick_locations)
axs_years.set_xticklabels(range(Investor.PAR_START_AGE, Investor.PAR_START_AGE + len(years_tick_locations)))
axs_years.set_xlabel('AGE (Years)'); axs0.spines['bottom'].set_visible(False)
3.3.3 Interpretation
In this episode the client chose to start with a plan at age 32 and to retire at age 65. Following this plan, the client is expected to have a networth of $3,181,499 at retirement. It is interesting how the agent learned not to assign any funds to luxury spending (magenta curve in top chart), as this does not lead to any reward (just the way the environment was setup). This could be addressed in an improved model of the investor/environment.
The agent recommends a strategy of first putting savings in the tax-deferred account each month until the annual government cap of $19,500 is reached (top chart). It then overpays on the home loan for the rest of the year. Note STA_HOME_REQ_PAYMENT, the required payment, is much lower (dotted red curve).
Once the home loan is paid off after only 3 and a half years, the agent substitutes taxable savings. The bottom chart shows the reward signal which is the month-to-month increase/decrease in networth.
Overall, the agent seems to have learned a sensible strategy.