Deep Reinforcement Learning
Deep Reinforcement Learning (DRL) is a subset of deep learning that involves training artificial agents to take actions in an environment to maximize a reward signal. In other words, it's a way to teach machines to learn and improve based on trial and error. DRL is often used in tasks such as game playing, robotics, and autonomous driving.
Here's an example of how to implement DRL using Python and the PyTorch library:
import gym
import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
# Define the environment
env = gym.make('CartPole-v1')
state_space_size = env.observation_space.shape[0]
action_space_size = env.action_space.n
# Define the neural network model
class QNet(nn.Module):
def __init__(self):
super(QNet, self).__init__()
self.fc1 = nn.Linear(state_space_size, 64)
self.fc2 = nn.Linear(64, 64)
self.fc3 = nn.Linear(64, action_space_size)
def forward(self, x):
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = self.fc3(x)
return x
# Define the agent
class DQNAgent:
def __init__(self, gamma=0.99, epsilon=1.0, epsilon_min=0.01, epsilon_decay=0.995, lr=0.001):
self.gamma = gamma
self.epsilon = epsilon
self.epsilon_min = epsilon_min
self.epsilon_decay = epsilon_decay
self.lr = lr
self.model = QNet()
self.optimizer = optim.Adam(self.model.parameters(), lr=self.lr)
def act(self, state):
if np.random.rand() <= self.epsilon:
return env.action_space.sample()
else:
state = torch.tensor(state, dtype=torch.float32)
q_values = self.model(state)
return torch.argmax(q_values).item()
def train(self, state, action, reward, next_state, done):
state = torch.tensor(state, dtype=torch.float32)
action = torch.tensor(action, dtype=torch.int64)
reward = torch.tensor(reward, dtype=torch.float32)
next_state = torch.tensor(next_state, dtype=torch.float32)
done = torch.tensor(done, dtype=torch.float32)
q_values = self.model(state)
next_q_values = self.model(next_state)
target = reward + (1 - done) * self.gamma * torch.max(next_q_values, dim=1)[0]
target = target.detach()
loss = nn.functional.mse_loss(q_values.gather(1, action.unsqueeze(1)), target.unsqueeze(1))
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
if self.epsilon > self.epsilon_min:
self.epsilon *= self.epsilon_decay
# Train the agent
agent = DQNAgent()
for episode in range(1000):
state = env.reset()
done = False
total_reward = 0
while not done:
action = agent.act(state)
next_state, reward, done, info = env.step(action)
agent.train(state, action, reward, next_state, done)
state = next_state
total_reward += reward
print("Episode {}: Total Reward = {}".format(episode, total_reward))
env.close()
In this example, we're using the gym library to create an environment (CartPole-v1) for the agent to interact with. We then define a neural network model (`QNet`) to represent the agent's Q-function, which maps states to actions and estimates the expected future reward. The agent selects actions based on an epsilon-greedy policy, where it chooses a random action with probability epsilon, or the action with the highest Q-value with probability (1 - epsilon).
During training, the agent interacts with the environment and collects experience tuples (state, action, reward, next_state, done), which are used to update the Q-function. We use the Bellman equation to estimate the expected future reward, and update the Q-values using the mean squared error loss.
The agent's epsilon value is decayed over time, so that it becomes more and more greedy as it gains experience. This allows the agent to explore the environment initially, but converge to a good policy over time.
In the main training loop, we run the agent in the environment for a fixed number of episodes, and print the total reward obtained in each episode. After training is complete, we close the environment.
Note that this is a simplified example, and there are many variations and extensions of DRL that are used in practice. However, this example should give you a sense of how DRL works and how it can be implemented using PyTorch.
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