The following code attempts to implement a Deep Q-Network (DQN) to solve the CartPole-v1 environment using OpenAI Gym. There are several implementation flaws that may prevent the agent from learning effectively.
Your task is to identify at least three critical issues in the code and suggest appropriate fixes.
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import gym
import torch
import torch.nn as nn
import torch.optim as optim
import random
import numpy as np
env = gym.make("CartPole-v1")
class DQN(nn.Module):
def __init__(self, state_dim, action_dim):
super(DQN, self).__init__()
self.network = nn.Sequential(
nn.Linear(state_dim, 128),
nn.ReLU(),
nn.Linear(128, 128),
nn.ReLU(),
nn.Linear(128, action_dim)
)
def forward(self, state):
return self.network(state)
state_dim = env.observation_space.shape[0]
action_dim = env.action_space.n
dqn = DQN(state_dim, action_dim)
optimizer = optim.Adam(dqn.parameters(), lr=0.001)
criterion = nn.MSELoss()
# Experience Replay Buffer
replay_buffer = []
def select_action(state, epsilon):
if random.random() < epsilon:
return env.action_space.sample()
state = torch.FloatTensor(state).unsqueeze(0)
q_values = dqn(state)
return torch.argmax(q_values).item()
for episode in range(100):
state = env.reset()
done = False
epsilon = max(0.01, 0.1 - episode / 200) # Epsilon decay
while not done:
action = select_action(state, epsilon)
next_state, reward, done, _ = env.step(action)
# Store experience in buffer
replay_buffer.append((state, action, reward, next_state, done))
# Sample a batch from replay buffer
if len(replay_buffer) > 32:
batch = random.sample(replay_buffer, 32)
state_batch, action_batch, reward_batch, next_state_batch, done_batch = zip(*batch)
state_batch = torch.FloatTensor(state_batch)
action_batch = torch.LongTensor(action_batch)
reward_batch = torch.FloatTensor(reward_batch)
next_state_batch = torch.FloatTensor(next_state_batch)
done_batch = torch.FloatTensor(done_batch)
q_values = dqn(state_batch)
next_q_values = dqn(next_state_batch).max(1)[0].detach()
target_q_values = reward_batch + (0.99 * next_q_values * (1 - done_batch))
loss = criterion(q_values.gather(1, action_batch.unsqueeze(1)).squeeze(), target_q_values)
optimizer.zero_grad()
loss.backward()
optimizer.step()
state = next_state
env.close()
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