Tutorial: Integration with TF-Agents RL Framework

[JaCoderX]

BTgym have two main sections, the Gym framework and the RL algorithm framework. The RL part is tailored to the unique gym requirements of BTgym, but as new research in the field is emerging there will be a benefit in exploring new algorithms that aren't implemented by this project.

The following tutorial is my own attempt of testing the integration between the Gym part of BTgym with an external RL framework. This tutorial is purely a Proof-of-Concept for testing this integration.

I took the most basic tutorial from the TF-Agent project - dqn tutorial and tried to run it with BTgym.

A few notes: DQN network has a simple implementation, it expect a simple array for action space and a simple array for observation space.

To resolve the action space issue, I have submitted a PR to TF-Agent that got rejected for basically being an overkill to the specification of the network. So you will need to manually apply those changes from here

To resolve the observation space you can manually collapse the dictionary or just work with 'external' as this is purely a proof of concept tutorial. I had changed this line py_environment.py to get only the external tag from the dictionary

[JaCoderX]

Tutorial code is a combination of TF-agent 1_dqn_tutorial and BTgym unreal_stacked_lstm_strat_4_11

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import warnings
warnings.filterwarnings("ignore") # suppress h5py deprecation warning

import os
import backtrader as bt
import numpy as np

from btgym import BTgymEnv, BTgymDataset
from btgym.strategy.observers import Reward, Position, NormPnL
from btgym.research.strategy_gen_4 import DevStrat_4_11

# Set backtesting engine parameters:

MyCerebro = bt.Cerebro()

# Define strategy and broker account parameters:
MyCerebro.addstrategy(
    DevStrat_4_11,
    start_cash=2000,  # initial broker cash
    commission=0.0001,  # commisssion to imitate spread
    leverage=10.0,
    order_size=2000,  # fixed stake, mind leverage
    drawdown_call=10, # max % to loose, in percent of initial cash
    target_call=10,  # max % to win, same
    skip_frame=10,
    gamma=0.99,
    reward_scale=7, # gardient`s nitrox, touch with care!
    state_ext_scale = np.linspace(3e3, 1e3, num=5)
)
# Visualisations for reward, position and PnL dynamics:
MyCerebro.addobserver(Reward)
MyCerebro.addobserver(Position)
MyCerebro.addobserver(NormPnL)

# Data: uncomment to get up to six month of 1 minute bars:
data_m1_6_month = [
    './data/DAT_ASCII_EURUSD_M1_201701.csv',
    './data/DAT_ASCII_EURUSD_M1_201702.csv',
    './data/DAT_ASCII_EURUSD_M1_201703.csv',
    './data/DAT_ASCII_EURUSD_M1_201704.csv',
    './data/DAT_ASCII_EURUSD_M1_201705.csv',
    './data/DAT_ASCII_EURUSD_M1_201706.csv',
]

# Uncomment single choice:
MyDataset = BTgymDataset(
    #filename=data_m1_6_month,
    filename='./data/test_sine_1min_period256_delta0002.csv',  # simple sine
    start_weekdays={0, 1, 2, 3, 4, 5, 6},
    episode_duration={'days': 1, 'hours': 23, 'minutes': 40}, # note: 2day-long episode
    start_00=False,
    time_gap={'hours': 10},
)

btgym_env = BTgymEnv(
    dataset=MyDataset,
    engine=MyCerebro,
    render_modes=['episode', 'human', 'internal', ],  # 'external'],
    render_state_as_image=True,
    render_ylabel='OHL_diff. / Internals',
    render_size_episode=(12, 8),
    render_size_human=(9, 4),
    render_size_state=(11, 3),
    render_dpi=75,
    port=5000,
    data_port=4999,
    connect_timeout=90,
    verbose=0,
)

import base64
import imageio
import IPython
import matplotlib
import matplotlib.pyplot as plt
import PIL.Image
import pyvirtualdisplay

import tensorflow as tf

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.utils import common

from tf_agents.environments import suite_gym

num_iterations = 20000 # @param {type:"integer"}

initial_collect_steps = 1000  # @param {type:"integer"}
collect_steps_per_iteration = 1  # @param {type:"integer"}
replay_buffer_max_length = 10000  # @param {type:"integer"}

batch_size = 64  # @param {type:"integer"}
learning_rate = 1e-3  # @param {type:"number"}
log_interval = 200  # @param {type:"integer"}

num_eval_episodes = 1  # @param {type:"integer"}
eval_interval = 1000  # @param {type:"integer"}

train_py_env = suite_gym.wrap_env(
    gym_env=btgym_env,
    #discount=1.0,
    #max_episode_steps=0,
    #gym_env_wrappers=(),
    #time_limit_wrapper=wrappers.TimeLimit,
    #env_wrappers=(),
    #spec_dtype_map=None,
    #auto_reset=True
)
eval_py_env = suite_gym.wrap_env(
    gym_env=btgym_env,
    #discount=1.0,
    #max_episode_steps=0,
    #gym_env_wrappers=(),
    #time_limit_wrapper=wrappers.TimeLimit,
    #env_wrappers=(),
    #spec_dtype_map=None,
    #auto_reset=True
)

train_env = tf_py_environment.TFPyEnvironment(train_py_env)
eval_env = tf_py_environment.TFPyEnvironment(eval_py_env)

observation_spec = train_env.observation_spec()
action_spec = train_env.action_spec()

print('Observation Spec:')
print(observation_spec)
print('Action Spec:')
print(action_spec)

fc_layer_params = (100,)

q_net = q_network.QNetwork(
    train_env.observation_spec(),
    train_env.action_spec(),
    #preprocessing_combiner=tf.keras.layers.Concatenate(axis=-1),
    fc_layer_params=fc_layer_params)

optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate)

train_step_counter = tf.Variable(0)

agent = dqn_agent.DqnAgent(
    train_env.time_step_spec(),
    train_env.action_spec(),
    q_network=q_net,
    optimizer=optimizer,
    td_errors_loss_fn=common.element_wise_squared_loss,
    train_step_counter=train_step_counter)

agent.initialize()

eval_policy = agent.policy
collect_policy = agent.collect_policy

random_policy = random_tf_policy.RandomTFPolicy(train_env.time_step_spec(), train_env.action_spec())

#time_step = train_env.reset()
#action = random_policy.action(time_step)
#print(action)
#print('ok')

replay_buffer = tf_uniform_replay_buffer.TFUniformReplayBuffer(
    data_spec=agent.collect_data_spec,
    batch_size=train_env.batch_size,
    max_length=replay_buffer_max_length
)

def compute_avg_return(environment, policy, num_episodes=10):

    import math

    total_return = 0.0
    for _ in range(num_episodes):

        time_step = environment.reset()
        episode_return = 0.0

        while not time_step.is_last():
            action_step = policy.action(time_step)
            time_step = environment.step(action_step.action)
            episode_return += time_step.reward
        total_return += episode_return

    avg_return = total_return / num_episodes
    return avg_return.numpy()[0]

def collect_step(environment, policy, buffer):
    time_step = environment.current_time_step()
    action_step = policy.action(time_step)
    next_time_step = environment.step(action_step.action)
    traj = trajectory.from_transition(time_step, action_step, next_time_step)

    # Add trajectory to the replay buffer
    buffer.add_batch(traj)

def collect_data(env, policy, buffer, steps):
    for _ in range(steps):
        collect_step(env, policy, buffer)

collect_data(train_env, random_policy, replay_buffer, steps=100)

#print(replay_buffer)
#print('ok')

# Dataset generates trajectories with shape [Bx2x...]
dataset = replay_buffer.as_dataset(
    num_parallel_calls=3,
    sample_batch_size=batch_size,
    num_steps=2).prefetch(3)

iterator = iter(dataset)

# (Optional) Optimize by wrapping some of the code in a graph using TF function.
agent.train = common.function(agent.train)

# # Reset the train step
agent.train_step_counter.assign(0)

# Evaluate the agent's policy once before training.
avg_return = compute_avg_return(eval_env, agent.policy, num_eval_episodes)
returns = [avg_return]

print(returns)

for _ in range(num_iterations):
    # TODO: added this reset cause when trying to collect_step for buffer it showed btgym the 'did reset hint' error
    # maybe it should be here in some form but not sure. for now just reset
  train_env.reset()

    # Collect a few steps using collect_policy and save to the replay buffer.
  for _ in range(collect_steps_per_iteration):
    collect_step(train_env, agent.collect_policy, replay_buffer)

  #print(train_env.action_spec())

  # Sample a batch of data from the buffer and update the agent's network.
  experience, unused_info = next(iterator)
  train_loss = agent.train(experience).loss

  step = agent.train_step_counter.numpy()

  if step % log_interval == 0:
    print('step = {0}: loss = {1}'.format(step, train_loss))

  if step % eval_interval == 0:
    avg_return = compute_avg_return(eval_env, agent.policy, num_eval_episodes)
    print('step = {0}: Average Return = {1}'.format(step, avg_return))
    returns.append(avg_return)

print('Done')

[Kismuz]

@JacobHanouna, Would you like to prepare pull request with tutorial notebook?

[JaCoderX]

I can make a notebook tutorial, but because there is some code that need be change for it to work. I thought it would be an issue, this is why I posted it here.

[Kismuz]

Ok, I see.