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This example shows how to train a DQN (Deep Q Networks) agent on the Cartpole environment using the TF-Agents library.
It will walk you through all the components in a Reinforcement Learning (RL) pipeline for training, evaluation and data collection.
To run this code live, click the 'Run in Google Colab' link above.
If you haven't installed the following dependencies, run:
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!sudo apt-get install -y xvfb ffmpeg
!pip install 'gym==0.10.11'
!pip install 'imageio==2.4.0'
!pip install PILLOW
!pip install 'pyglet==1.3.2'
!pip install pyvirtualdisplay
!pip install --pre tf-agents[reverb]
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from __future__ import absolute_import, division, print_function
import base64
import imageio
import IPython
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
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
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tf.compat.v1.enable_v2_behavior()
# Set up a virtual display for rendering OpenAI gym environments.
display = pyvirtualdisplay.Display(visible=0, size=(1400, 900)).start()
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tf.version.VERSION
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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 = 100000 # @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 = 10 # @param {type:"integer"}
eval_interval = 1000 # @param {type:"integer"}
In Reinforcement Learning (RL), an environment represents the task or problem to be solved. Standard environments can be created in TF-Agents using tf_agents.environments suites. TF-Agents has suites for loading environments from sources such as the OpenAI Gym, Atari, and DM Control.
Load the CartPole environment from the OpenAI Gym suite.
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env_name = 'CartPole-v0'
env = suite_gym.load(env_name)
You can render this environment to see how it looks. A free-swinging pole is attached to a cart. The goal is to move the cart right or left in order to keep the pole pointing up.
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#@test {"skip": true}
env.reset()
PIL.Image.fromarray(env.render())
The environment.step method takes an action in the environment and returns a TimeStep tuple containing the next observation of the environment and the reward for the action.
The time_step_spec() method returns the specification for the TimeStep tuple. Its observation attribute shows the shape of observations, the data types, and the ranges of allowed values. The reward attribute shows the same details for the reward.
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print('Observation Spec:')
print(env.time_step_spec().observation)
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print('Reward Spec:')
print(env.time_step_spec().reward)
The action_spec() method returns the shape, data types, and allowed values of valid actions.
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print('Action Spec:')
print(env.action_spec())
In the Cartpole environment:
observation is an array of 4 floats: reward is a scalar float valueaction is a scalar integer with only two possible values:0 — "move left"1 — "move right"
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time_step = env.reset()
print('Time step:')
print(time_step)
action = np.array(1, dtype=np.int32)
next_time_step = env.step(action)
print('Next time step:')
print(next_time_step)
Usually two environments are instantiated: one for training and one for evaluation.
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train_py_env = suite_gym.load(env_name)
eval_py_env = suite_gym.load(env_name)
The Cartpole environment, like most environments, is written in pure Python. This is converted to TensorFlow using the TFPyEnvironment wrapper.
The original environment's API uses Numpy arrays. The TFPyEnvironment converts these to Tensors to make it compatible with Tensorflow agents and policies.
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train_env = tf_py_environment.TFPyEnvironment(train_py_env)
eval_env = tf_py_environment.TFPyEnvironment(eval_py_env)
The algorithm used to solve an RL problem is represented by an Agent. TF-Agents provides standard implementations of a variety of Agents, including:
The DQN agent can be used in any environment which has a discrete action space.
At the heart of a DQN Agent is a QNetwork, a neural network model that can learn to predict QValues (expected returns) for all actions, given an observation from the environment.
Use tf_agents.networks.q_network to create a QNetwork, passing in the observation_spec, action_spec, and a tuple describing the number and size of the model's hidden layers.
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fc_layer_params = (100,)
q_net = q_network.QNetwork(
train_env.observation_spec(),
train_env.action_spec(),
fc_layer_params=fc_layer_params)
Now use tf_agents.agents.dqn.dqn_agent to instantiate a DqnAgent. In addition to the time_step_spec, action_spec and the QNetwork, the agent constructor also requires an optimizer (in this case, AdamOptimizer), a loss function, and an integer step counter.
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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()
A policy defines the way an agent acts in an environment. Typically, the goal of reinforcement learning is to train the underlying model until the policy produces the desired outcome.
In this tutorial:
time_step observation.Agents contain two policies:
agent.policy — The main policy that is used for evaluation and deployment.agent.collect_policy — A second policy that is used for data collection.
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eval_policy = agent.policy
collect_policy = agent.collect_policy
Policies can be created independently of agents. For example, use tf_agents.policies.random_tf_policy to create a policy which will randomly select an action for each time_step.
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random_policy = random_tf_policy.RandomTFPolicy(train_env.time_step_spec(),
train_env.action_spec())
To get an action from a policy, call the policy.action(time_step) method. The time_step contains the observation from the environment. This method returns a PolicyStep, which is a named tuple with three components:
action — the action to be taken (in this case, 0 or 1)state — used for stateful (that is, RNN-based) policiesinfo — auxiliary data, such as log probabilities of actions
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example_environment = tf_py_environment.TFPyEnvironment(
suite_gym.load('CartPole-v0'))
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time_step = example_environment.reset()
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random_policy.action(time_step)
The most common metric used to evaluate a policy is the average return. The return is the sum of rewards obtained while running a policy in an environment for an episode. Several episodes are run, creating an average return.
The following function computes the average return of a policy, given the policy, environment, and a number of episodes.
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#@test {"skip": true}
def compute_avg_return(environment, policy, num_episodes=10):
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]
# See also the metrics module for standard implementations of different metrics.
# https://github.com/tensorflow/agents/tree/master/tf_agents/metrics
Running this computation on the random_policy shows a baseline performance in the environment.
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compute_avg_return(eval_env, random_policy, num_eval_episodes)
The replay buffer keeps track of data collected from the environment. This tutorial uses tf_agents.replay_buffers.tf_uniform_replay_buffer.TFUniformReplayBuffer, as it is the most common.
The constructor requires the specs for the data it will be collecting. This is available from the agent using the collect_data_spec method. The batch size and maximum buffer length are also required.
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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)
For most agents, collect_data_spec is a named tuple called Trajectory, containing the specs for observations, actions, rewards, and other items.
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agent.collect_data_spec
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agent.collect_data_spec._fields
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#@test {"skip": true}
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)
# This loop is so common in RL, that we provide standard implementations.
# For more details see the drivers module.
# https://www.tensorflow.org/agents/api_docs/python/tf_agents/drivers
The replay buffer is now a collection of Trajectories.
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# For the curious:
# Uncomment to peel one of these off and inspect it.
# iter(replay_buffer.as_dataset()).next()
The agent needs access to the replay buffer. This is provided by creating an iterable tf.data.Dataset pipeline which will feed data to the agent.
Each row of the replay buffer only stores a single observation step. But since the DQN Agent needs both the current and next observation to compute the loss, the dataset pipeline will sample two adjacent rows for each item in the batch (num_steps=2).
This dataset is also optimized by running parallel calls and prefetching data.
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# 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)
dataset
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iterator = iter(dataset)
print(iterator)
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# For the curious:
# Uncomment to see what the dataset iterator is feeding to the agent.
# Compare this representation of replay data
# to the collection of individual trajectories shown earlier.
# iterator.next()
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#@test {"skip": true}
try:
%%time
except:
pass
# (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]
for _ in range(num_iterations):
# 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)
# 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)
Use matplotlib.pyplot to chart how the policy improved during training.
One iteration of Cartpole-v0 consists of 200 time steps. The environment gives a reward of +1 for each step the pole stays up, so the maximum return for one episode is 200. The charts shows the return increasing towards that maximum each time it is evaluated during training. (It may be a little unstable and not increase monotonically each time.)
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#@test {"skip": true}
iterations = range(0, num_iterations + 1, eval_interval)
plt.plot(iterations, returns)
plt.ylabel('Average Return')
plt.xlabel('Iterations')
plt.ylim(top=250)
Charts are nice. But more exciting is seeing an agent actually performing a task in an environment.
First, create a function to embed videos in the notebook.
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def embed_mp4(filename):
"""Embeds an mp4 file in the notebook."""
video = open(filename,'rb').read()
b64 = base64.b64encode(video)
tag = '''
<video width="640" height="480" controls>
<source src="data:video/mp4;base64,{0}" type="video/mp4">
Your browser does not support the video tag.
</video>'''.format(b64.decode())
return IPython.display.HTML(tag)
Now iterate through a few episodes of the Cartpole game with the agent. The underlying Python environment (the one "inside" the TensorFlow environment wrapper) provides a render() method, which outputs an image of the environment state. These can be collected into a video.
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def create_policy_eval_video(policy, filename, num_episodes=5, fps=30):
filename = filename + ".mp4"
with imageio.get_writer(filename, fps=fps) as video:
for _ in range(num_episodes):
time_step = eval_env.reset()
video.append_data(eval_py_env.render())
while not time_step.is_last():
action_step = policy.action(time_step)
time_step = eval_env.step(action_step.action)
video.append_data(eval_py_env.render())
return embed_mp4(filename)
create_policy_eval_video(agent.policy, "trained-agent")
For fun, compare the trained agent (above) to an agent moving randomly. (It does not do as well.)
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create_policy_eval_video(random_policy, "random-agent")