Deep Dive into Reinforcement Learning: Solving the CartPole Problem with PyTorch

PyTorch has emerged as one of the leading frameworks in the field of deep learning. Its versatility makes it applicable in various domains, including data science, machine learning, and even reinforcement learning. In a previous article, we extensively covered the process of building reinforcement learning models using TensorFlow. In this article, we will shift our focus to PyTorch and explore how we can leverage it to create effective reinforcement learning models.

The CartPole Problem

To illustrate the concepts, we will dive into the CartPole problem. This problem involves training an agent to make decisions on whether to move a cart to the left or right, such that a pole attached to the cart remains balanced and upright.

In this problem, an agent’s action depends on the current state of the environment. As the environment evolves, it provides rewards to the agent, which influence the agent’s decision-making process. Specifically, we will assign a reward of +1 at every timestep. However, if the cart moves more than a predefined limit from the center (e.g., 2.4 units), or if the pole falls over too far, the environment will not provide any reward. This setup encourages the agent to perform well and achieve larger rewards by maintaining balance over a longer duration.

Implementation Using PyTorch

To begin with, we need to install the necessary libraries, including gym and pytorch. If you are using Google Colab, these libraries are likely pre-installed. Otherwise, you can install them using the following commands:

!pip install gym
!pip install pytorch

Once the required libraries are installed, we can proceed with importing them into our environment. The following libraries are crucial for our reinforcement learning process:

import gym
import math
import random
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from collections import namedtuple, deque
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
import torchvision.transforms as T

With these packages in place, we can define the necessary setup for our reinforcement learning process. This includes unwrapping the CartPole-v0 environment, setting up matplotlib for visualization, and determining whether to use a CPU or GPU for computation:

env = gym.make('CartPole-v0').unwrapped
is_ipython = 'inline' in matplotlib.get_backend()
if is_ipython:
    from IPython import display
plt.ion()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

Storing Memory

Reinforcement learning typically requires a memory buffer to store recent observations, which can be utilized for training the network. We define two classes to handle memory storage:

pythonCopy codeTransition = namedtuple('Transition', ('state', 'action', 'next_state', 'reward'))

class ReplayMemory(object):
    def __init__(self, capacity):
        self.memory = deque([], maxlen=capacity)
        
    def push(self, *args):
        self.memory.append(Transition(*args))
        
    def sample(self, batch_size):
        return random.sample(self.memory, batch_size)
        
    def __len__(self):
        return len(self.memory)

The Transition class represents a single transition in the environment. It stores the state, action, next state, and reward. On the other hand, the ReplayMemory class acts as a buffer for storing these transitions. It allows pushing new transitions, sampling a batch of transitions for training, and providing the current memory size.

Deep Q-Network Algorithm

In our reinforcement learning procedure, we aim to train an agent based on a policy that maximizes the cumulative reward. The Q-learning algorithm plays a significant role in achieving this objective. It involves utilizing a function that estimates the expected return for each action.

For our network architecture, we will use a convolutional neural network (CNN) consisting of 5 convolutional layers. PyTorch provides the necessary functionality to define such a network:

class DQN(nn.Module):
    def __init__(self, h, w, outputs):
        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)
        
        def conv2d_size_out(size, kernel_size=5, stride=2):
            return (size - (kernel_size - 1) - 1) // stride + 1
        
        convw = conv2d_size_out(conv2d_size_out(conv2d_size_out(w)))
        convh = conv2d_size_out(conv2d_size_out(conv2d_size_out(h)))
        linear_input_size = convw * convh * 32
        self.head = nn.Linear(linear_input_size, outputs)
        
    def forward(self, x):
        x = x.to(device)
        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))

The DQN class inherits from nn.Module and defines the network architecture. It consists of three convolutional layers followed by a fully connected layer. The output of the network represents the expected return for each possible action given the current state.

Training the Network

After setting up the model and optimizer, we can begin the training process. Here are the key steps involved:

1. Defining Parameters: We specify various parameters such as batch size, discount factor (gamma), exploration rate (epsilon), and target update frequency.
2. Initializing the Environment: We retrieve the screen size and the number of actions from the CartPole environment.
3. Defining the Policy and Target Networks: We instantiate the policy network and the target network. The target network is initialized with the same parameters as the policy network and is updated periodically.
4. Defining the Optimizer: We use the RMSprop optimizer to update the policy network’s parameters.
5. Storing Memory: We create an instance of the replay memory buffer to store transitions observed by the agent.
6. Selecting Actions: We define a utility function to select actions based on an epsilon-greedy policy.
7. Plotting Durations: We define a utility function to plot the duration of episodes during training.
8. Optimization Loop: We define a function optimize_model() to perform one optimization step for the policy network.
9. Training Loop: We iterate over a fixed number of episodes and perform the training process, including selecting actions, updating the replay memory, and optimizing the model.
10. Updating the Target Network: Every few episodes, we update the target network by loading the parameters from the policy network.
11. Completing the Training: Once the training loop is complete, we can render the final result and visualize the CartPole environment.

Final Words: We conclude the article by summarizing the key points discussed and highlighting the importance of PyTorch in building reinforcement learning models.