.. _sphx_glr_intermediate_reinforcement_q_learning.py:
Reinforcement Learning (DQN) tutorial
=====================================
**Author**: `Adam Paszke `_
This tutorial shows how to use PyTorch to train a Deep Q Learning (DQN) agent
on the CartPole-v0 task from the `OpenAI Gym `__.
**Task**
The agent has to decide between two actions - moving the cart left or
right - so that the pole attached to it stays upright. You can find an
official leaderboard with various algorithms and visualizations at the
`Gym website `__.
.. figure:: /_static/img/cartpole.gif
:alt: cartpole
cartpole
As the agent observes the current state of the environment and chooses
an action, the environment *transitions* to a new state, and also
returns a reward that indicates the consequences of the action. In this
task, the environment terminates if the pole falls over too far.
The CartPole task is designed so that the inputs to the agent are 4 real
values representing the environment state (position, velocity, etc.).
However, neural networks can solve the task purely by looking at the
scene, so we'll use a patch of the screen centered on the cart as an
input. Because of this, our results aren't directly comparable to the
ones from the official leaderboard - our task is much harder.
Unfortunately this does slow down the training, because we have to
render all the frames.
Strictly speaking, we will present the state as the difference between
the current screen patch and the previous one. This will allow the agent
to take the velocity of the pole into account from one image.
**Packages**
First, let's import needed packages. Firstly, we need
`gym `__ for the environment
(Install using `pip install gym`).
We'll also use the following from PyTorch:
- neural networks (``torch.nn``)
- optimization (``torch.optim``)
- automatic differentiation (``torch.autograd``)
- utilities for vision tasks (``torchvision`` - `a separate
package `__).
.. code-block:: python
import gym
import math
import random
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from collections import namedtuple
from itertools import count
from PIL import Image
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
import torchvision.transforms as T
env = gym.make('CartPole-v0').unwrapped
# set up matplotlib
is_ipython = 'inline' in matplotlib.get_backend()
if is_ipython:
from IPython import display
plt.ion()
# if gpu is to be used
use_cuda = torch.cuda.is_available()
FloatTensor = torch.cuda.FloatTensor if use_cuda else torch.FloatTensor
LongTensor = torch.cuda.LongTensor if use_cuda else torch.LongTensor
ByteTensor = torch.cuda.ByteTensor if use_cuda else torch.ByteTensor
Tensor = FloatTensor
Replay Memory
-------------
We'll be using experience replay memory for training our DQN. It stores
the transitions that the agent observes, allowing us to reuse this data
later. By sampling from it randomly, the transitions that build up a
batch are decorrelated. It has been shown that this greatly stabilizes
and improves the DQN training procedure.
For this, we're going to need two classses:
- ``Transition`` - a named tuple representing a single transition in
our environment
- ``ReplayMemory`` - a cyclic buffer of bounded size that holds the
transitions observed recently. It also implements a ``.sample()``
method for selecting a random batch of transitions for training.
.. code-block:: python
Transition = namedtuple('Transition',
('state', 'action', 'next_state', 'reward'))
class ReplayMemory(object):
def __init__(self, capacity):
self.capacity = capacity
self.memory = []
self.position = 0
def push(self, *args):
"""Saves a transition."""
if len(self.memory) < self.capacity:
self.memory.append(None)
self.memory[self.position] = Transition(*args)
self.position = (self.position + 1) % self.capacity
def sample(self, batch_size):
return random.sample(self.memory, batch_size)
def __len__(self):
return len(self.memory)
Now, let's define our model. But first, let quickly recap what a DQN is.
DQN algorithm
-------------
Our environment is deterministic, so all equations presented here are
also formulated deterministically for the sake of simplicity. In the
reinforcement learning literature, they would also contain expectations
over stochastic transitions in the environment.
Our aim will be to train a policy that tries to maximize the discounted,
cumulative reward
:math:`R_{t_0} = \sum_{t=t_0}^{\infty} \gamma^{t - t_0} r_t`, where
:math:`R_{t_0}` is also known as the *return*. The discount,
:math:`\gamma`, should be a constant between :math:`0` and :math:`1`
that ensures the sum converges. It makes rewards from the uncertain far
future less important for our agent than the ones in the near future
that it can be fairly confident about.
The main idea behind Q-learning is that if we had a function
:math:`Q^*: State \times Action \rightarrow \mathbb{R}`, that could tell
us what our return would be, if we were to take an action in a given
state, then we could easily construct a policy that maximizes our
rewards:
.. math:: \pi^*(s) = \arg\!\max_a \ Q^*(s, a)
However, we don't know everything about the world, so we don't have
access to :math:`Q^*`. But, since neural networks are universal function
approximators, we can simply create one and train it to resemble
:math:`Q^*`.
For our training update rule, we'll use a fact that every :math:`Q`
function for some policy obeys the Bellman equation:
.. math:: Q^{\pi}(s, a) = r + \gamma Q^{\pi}(s', \pi(s'))
The difference between the two sides of the equality is known as the
temporal difference error, :math:`\delta`:
.. math:: \delta = Q(s, a) - (r + \gamma \max_a Q(s', a))
To minimise this error, we will use the `Huber
loss `__. The Huber loss acts
like the mean squared error when the error is small, but like the mean
absolute error when the error is large - this makes it more robust to
outliers when the estimates of :math:`Q` are very noisy. We calculate
this over a batch of transitions, :math:`B`, sampled from the replay
memory:
.. math::
\mathcal{L} = \frac{1}{|B|}\sum_{(s, a, s', r) \ \in \ B} \mathcal{L}(\delta)
.. math::
\text{where} \quad \mathcal{L}(\delta) = \begin{cases}
\frac{1}{2}{\delta^2} & \text{for } |\delta| \le 1, \\
|\delta| - \frac{1}{2} & \text{otherwise.}
\end{cases}
Q-network
^^^^^^^^^
Our model will be a convolutional neural network that takes in the
difference between the current and previous screen patches. It has two
outputs, representing :math:`Q(s, \mathrm{left})` and
:math:`Q(s, \mathrm{right})` (where :math:`s` is the input to the
network). In effect, the network is trying to predict the *quality* of
taking each action given the current input.
.. code-block:: python
class DQN(nn.Module):
def __init__(self):
super(DQN, self).__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=5, stride=2)
self.bn1 = nn.BatchNorm2d(16)
self.conv2 = nn.Conv2d(16, 32, kernel_size=5, stride=2)
self.bn2 = nn.BatchNorm2d(32)
self.conv3 = nn.Conv2d(32, 32, kernel_size=5, stride=2)
self.bn3 = nn.BatchNorm2d(32)
self.head = nn.Linear(448, 2)
def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x)))
x = F.relu(self.bn3(self.conv3(x)))
return self.head(x.view(x.size(0), -1))
Input extraction
^^^^^^^^^^^^^^^^
The code below are utilities for extracting and processing rendered
images from the environment. It uses the ``torchvision`` package, which
makes it easy to compose image transforms. Once you run the cell it will
display an example patch that it extracted.
.. code-block:: python
resize = T.Compose([T.ToPILImage(),
T.Resize(40, interpolation=Image.CUBIC),
T.ToTensor()])
# This is based on the code from gym.
screen_width = 600
def get_cart_location():
world_width = env.x_threshold * 2
scale = screen_width / world_width
return int(env.state[0] * scale + screen_width / 2.0) # MIDDLE OF CART
def get_screen():
screen = env.render(mode='rgb_array').transpose(
(2, 0, 1)) # transpose into torch order (CHW)
# Strip off the top and bottom of the screen
screen = screen[:, 160:320]
view_width = 320
cart_location = get_cart_location()
if cart_location < view_width // 2:
slice_range = slice(view_width)
elif cart_location > (screen_width - view_width // 2):
slice_range = slice(-view_width, None)
else:
slice_range = slice(cart_location - view_width // 2,
cart_location + view_width // 2)
# Strip off the edges, so that we have a square image centered on a cart
screen = screen[:, :, slice_range]
# Convert to float, rescare, convert to torch tensor
# (this doesn't require a copy)
screen = np.ascontiguousarray(screen, dtype=np.float32) / 255
screen = torch.from_numpy(screen)
# Resize, and add a batch dimension (BCHW)
return resize(screen).unsqueeze(0).type(Tensor)
env.reset()
plt.figure()
plt.imshow(get_screen().cpu().squeeze(0).permute(1, 2, 0).numpy(),
interpolation='none')
plt.title('Example extracted screen')
plt.show()
Training
--------
Hyperparameters and utilities
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
This cell instantiates our model and its optimizer, and defines some
utilities:
- ``Variable`` - this is a simple wrapper around
``torch.autograd.Variable`` that will automatically send the data to
the GPU every time we construct a Variable.
- ``select_action`` - will select an action accordingly to an epsilon
greedy policy. Simply put, we'll sometimes use our model for choosing
the action, and sometimes we'll just sample one uniformly. The
probability of choosing a random action will start at ``EPS_START``
and will decay exponentially towards ``EPS_END``. ``EPS_DECAY``
controls the rate of the decay.
- ``plot_durations`` - a helper for plotting the durations of episodes,
along with an average over the last 100 episodes (the measure used in
the official evaluations). The plot will be underneath the cell
containing the main training loop, and will update after every
episode.
.. code-block:: python
BATCH_SIZE = 128
GAMMA = 0.999
EPS_START = 0.9
EPS_END = 0.05
EPS_DECAY = 200
TARGET_UPDATE = 10
policy_net = DQN()
target_net = DQN()
target_net.load_state_dict(policy_net.state_dict())
target_net.eval()
if use_cuda:
policy_net.cuda()
target_net.cuda()
optimizer = optim.RMSprop(policy_net.parameters())
memory = ReplayMemory(10000)
steps_done = 0
def select_action(state):
global steps_done
sample = random.random()
eps_threshold = EPS_END + (EPS_START - EPS_END) * \
math.exp(-1. * steps_done / EPS_DECAY)
steps_done += 1
if sample > eps_threshold:
return policy_net(
Variable(state, volatile=True).type(FloatTensor)).data.max(1)[1].view(1, 1)
else:
return LongTensor([[random.randrange(2)]])
episode_durations = []
def plot_durations():
plt.figure(2)
plt.clf()
durations_t = torch.FloatTensor(episode_durations)
plt.title('Training...')
plt.xlabel('Episode')
plt.ylabel('Duration')
plt.plot(durations_t.numpy())
# Take 100 episode averages and plot them too
if len(durations_t) >= 100:
means = durations_t.unfold(0, 100, 1).mean(1).view(-1)
means = torch.cat((torch.zeros(99), means))
plt.plot(means.numpy())
plt.pause(0.001) # pause a bit so that plots are updated
if is_ipython:
display.clear_output(wait=True)
display.display(plt.gcf())
Training loop
^^^^^^^^^^^^^
Finally, the code for training our model.
Here, you can find an ``optimize_model`` function that performs a
single step of the optimization. It first samples a batch, concatenates
all the tensors into a single one, computes :math:`Q(s_t, a_t)` and
:math:`V(s_{t+1}) = \max_a Q(s_{t+1}, a)`, and combines them into our
loss. By defition we set :math:`V(s) = 0` if :math:`s` is a terminal
state. We also use a target network to compute :math:`V(s_{t+1})` for
added stability. The target network has its weights kept frozen most of
the time, but is updated with the policy network's weights every so often.
This is usually a set number of steps but we shall use episodes for
simplicity.
.. code-block:: python
def optimize_model():
if len(memory) < BATCH_SIZE:
return
transitions = memory.sample(BATCH_SIZE)
# Transpose the batch (see http://stackoverflow.com/a/19343/3343043 for
# detailed explanation).
batch = Transition(*zip(*transitions))
# Compute a mask of non-final states and concatenate the batch elements
non_final_mask = ByteTensor(tuple(map(lambda s: s is not None,
batch.next_state)))
non_final_next_states = Variable(torch.cat([s for s in batch.next_state
if s is not None]),
volatile=True)
state_batch = Variable(torch.cat(batch.state))
action_batch = Variable(torch.cat(batch.action))
reward_batch = Variable(torch.cat(batch.reward))
# Compute Q(s_t, a) - the model computes Q(s_t), then we select the
# columns of actions taken
state_action_values = policy_net(state_batch).gather(1, action_batch)
# Compute V(s_{t+1}) for all next states.
next_state_values = Variable(torch.zeros(BATCH_SIZE).type(Tensor))
next_state_values[non_final_mask] = target_net(non_final_next_states).max(1)[0]
# Compute the expected Q values
expected_state_action_values = (next_state_values * GAMMA) + reward_batch
# Undo volatility (which was used to prevent unnecessary gradients)
expected_state_action_values = Variable(expected_state_action_values.data)
# Compute Huber loss
loss = F.smooth_l1_loss(state_action_values, expected_state_action_values)
# Optimize the model
optimizer.zero_grad()
loss.backward()
for param in policy_net.parameters():
param.grad.data.clamp_(-1, 1)
optimizer.step()
Below, you can find the main training loop. At the beginning we reset
the environment and initialize the ``state`` variable. Then, we sample
an action, execute it, observe the next screen and the reward (always
1), and optimize our model once. When the episode ends (our model
fails), we restart the loop.
Below, `num_episodes` is set small. You should download
the notebook and run lot more epsiodes.
.. code-block:: python
num_episodes = 50
for i_episode in range(num_episodes):
# Initialize the environment and state
env.reset()
last_screen = get_screen()
current_screen = get_screen()
state = current_screen - last_screen
for t in count():
# Select and perform an action
action = select_action(state)
_, reward, done, _ = env.step(action[0, 0])
reward = Tensor([reward])
# Observe new state
last_screen = current_screen
current_screen = get_screen()
if not done:
next_state = current_screen - last_screen
else:
next_state = None
# Store the transition in memory
memory.push(state, action, next_state, reward)
# Move to the next state
state = next_state
# Perform one step of the optimization (on the target network)
optimize_model()
if done:
episode_durations.append(t + 1)
plot_durations()
break
# Update the target network
if i_episode % TARGET_UPDATE == 0:
target_net.load_state_dict(policy_net.state_dict())
print('Complete')
env.render(close=True)
env.close()
plt.ioff()
plt.show()
**Total running time of the script:** ( 0 minutes 0.000 seconds)
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