Pytorch Deep Reinforcement Learning Tutorial (With Code!)
Deep reinforcement learning is a subfield of machine learning that focuses on training agents to make sequential decisions in an environment to achieve a particular goal. Pytorch is a popular deep learning library with strong support for building neural networks and training models.
In this tutorial, we will walk through the steps of building a deep reinforcement learning model using Pytorch. We will use the OpenAI Gym environment CartPole, where the agent’s goal is to balance a pole on a cart by moving it left or right.
Step 1: Install Pytorch and OpenAI Gym
Before getting started, make sure you have Pytorch and OpenAI Gym installed on your system. You can install them using pip:
pip install torch
pip install gym
Step 2: Define the Deep Q-Network
We will use a Deep Q-Network (DQN) to train our agent. DQN is a type of neural network that approximates the Q-function in reinforcement learning. Here’s an example of how you can define a DQN in Pytorch:
import torch
import torch.nn as nn
import torch.nn.functional as F
class DQN(nn.Module):
def __init__(self, obs_dim, action_dim):
super(DQN, self).__init__()
self.fc1 = nn.Linear(obs_dim, 128)
self.fc2 = nn.Linear(128, 128)
self.fc3 = nn.Linear(128, action_dim)
def forward(self, x):
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
Step 3: Implement the Agent
Next, we will implement the agent that interacts with the environment using the DQN. Here’s an example of how you can define an agent in Pytorch:
import numpy as np
import random
class Agent:
def __init__(self, obs_dim, action_dim):
self.obs_dim = obs_dim
self.action_dim = action_dim
self.dqn = DQN(obs_dim, action_dim)
self.target_dqn = DQN(obs_dim, action_dim)
def act(self, obs):
obs = torch.tensor(obs, dtype=torch.float32).unsqueeze(0)
action_values = self.dqn(obs)
return torch.argmax(action_values).item()
def experience_replay(self, memory, batch_size, discount_factor):
if len(memory) < batch_size:
return
batch = random.sample(memory, batch_size)
states, actions, rewards, next_states, dones = zip(*batch)
states = torch.tensor(states, dtype=torch.float32)
actions = torch.tensor(actions, dtype=torch.int64).unsqueeze(1)
rewards = torch.tensor(rewards, dtype=torch.float32).unsqueeze(1)
next_states = torch.tensor(next_states, dtype=torch.float32)
dones = torch.tensor(dones, dtype=torch.float32).unsqueeze(1)
Step 4: Train the Agent
Finally, we will train our agent using the DQN and the CartPole environment. Here’s an example of how you can train the agent in Pytorch:
import gym
env = gym.make('CartPole-v1')
obs_dim = env.observation_space.shape[0]
action_dim = env.action_space.n
agent = Agent(obs_dim, action_dim)
for episode in range(100):
obs = env.reset()
total_reward = 0
done = False
while not done:
action = agent.act(obs)
next_obs, reward, done, _ = env.step(action)
agent.experience_replay(memory, batch_size, discount_factor)
obs = next_obs
total_reward += reward
print(f'Episode {episode}, Total Reward: {total_reward}')
And that’s it! You have now built and trained a deep reinforcement learning agent using Pytorch. Feel free to experiment with different hyperparameters and architectures to improve the performance of your agent.
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Hello hello
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