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| 1 | +import gymnasium as gym |
| 2 | +import torch |
| 3 | +import numpy as np |
| 4 | +import torch.nn as nn |
| 5 | +import torch.optim as optim |
| 6 | +from torch.utils.data import Dataset, DataLoader |
| 7 | + |
| 8 | +# Define the PPO agent |
| 9 | +class PPOAgent(nn.Module): |
| 10 | + def __init__(self, state_dim, action_dim): |
| 11 | + super(PPOAgent, self).__init__() |
| 12 | + self.policy_network = nn.Sequential( |
| 13 | + nn.Linear(state_dim, 128), |
| 14 | + nn.ReLU(), |
| 15 | + nn.Linear(128, 128), |
| 16 | + nn.ReLU(), |
| 17 | + nn.Linear(128, action_dim) |
| 18 | + ) |
| 19 | + self.value_network = nn.Sequential( |
| 20 | + nn.Linear(state_dim, 128), |
| 21 | + nn.ReLU(), |
| 22 | + nn.Linear(128, 128), |
| 23 | + nn.ReLU(), |
| 24 | + nn.Linear(128, 1) |
| 25 | + ) |
| 26 | + |
| 27 | + def forward(self, state): |
| 28 | + policy_output = self.policy_network(state) |
| 29 | + value_output = self.value_network(state) |
| 30 | + return policy_output, value_output |
| 31 | + |
| 32 | +# Define the priority network |
| 33 | +class PriorityNetwork(nn.Module): |
| 34 | + def __init__(self, state_dim, action_dim): |
| 35 | + super(PriorityNetwork, self).__init__() |
| 36 | + self.priority_network = nn.Sequential( |
| 37 | + nn.Linear(state_dim + action_dim + 1 + state_dim + 1, 128), |
| 38 | + nn.ReLU(), |
| 39 | + nn.Linear(128, 1) |
| 40 | + ) |
| 41 | + |
| 42 | + def forward(self, experience): |
| 43 | + priority_output = self.priority_network(experience) |
| 44 | + return priority_output |
| 45 | + |
| 46 | +# Define the PPO trainer |
| 47 | +class PPOTrainer: |
| 48 | + |
| 49 | + def __init__(self, agent, priority_network, gamma, lambda_, epsilon, c1, c2): |
| 50 | + self.agent = agent |
| 51 | + self.priority_network = priority_network |
| 52 | + self.gamma = gamma |
| 53 | + self.lambda_ = lambda_ |
| 54 | + self.epsilon = epsilon |
| 55 | + self.c1 = c1 |
| 56 | + self.c2 = c2 |
| 57 | + |
| 58 | + def train(self, batch_size, epochs): |
| 59 | + for epoch in range(epochs): |
| 60 | + # Sample a batch of experiences from the replay buffer |
| 61 | + batch_experiences = self.sample_batch(batch_size) |
| 62 | + |
| 63 | + # Compute the TD-error for each experience in the batch |
| 64 | + td_errors = [] |
| 65 | + for experience in batch_experiences: |
| 66 | + state, action, reward, next_state, done = experience |
| 67 | + td_error = reward + self.gamma * self.agent.value_network(next_state) - self.agent.value_network(state) |
| 68 | + td_errors.append(td_error) |
| 69 | + |
| 70 | + # Train the priority network |
| 71 | + self.priority_network.train() |
| 72 | + priority_optimizer = optim.Adam(self.priority_network.parameters(), lr=0.001) |
| 73 | + priority_loss_fn = nn.MSELoss() |
| 74 | + for experience, td_error in zip(batch_experiences, td_errors): |
| 75 | + priority_optimizer.zero_grad() |
| 76 | + priority_output = self.priority_network(experience) |
| 77 | + loss = priority_loss_fn(priority_output, torch.tensor(td_error)) |
| 78 | + loss.backward() |
| 79 | + priority_optimizer.step() |
| 80 | + |
| 81 | + # Train the PPO agent |
| 82 | + self.agent.train() |
| 83 | + policy_optimizer = optim.Adam(self.agent.policy_network.parameters(), lr=0.001) |
| 84 | + value_optimizer = optim.Adam(self.agent.value_network.parameters(), lr=0.001) |
| 85 | + for experience in batch_experiences: |
| 86 | + state, action, reward, next_state, done = experience |
| 87 | + policy_optimizer.zero_grad() |
| 88 | + value_optimizer.zero_grad() |
| 89 | + policy_output, value_output = self.agent(state) |
| 90 | + policy_loss = -torch.log(policy_output[action]) * reward |
| 91 | + value_loss = (value_output - reward) ** 2 |
| 92 | + loss = policy_loss + value_loss |
| 93 | + loss.backward() |
| 94 | + policy_optimizer.step() |
| 95 | + value_optimizer.step() |
| 96 | + |
| 97 | + def sample_batch(self, batch_size): |
| 98 | + # Sample a batch of experiences from the replay buffer |
| 99 | + # This is a placeholder for the actual sampling logic |
| 100 | + batch_experiences = [] |
| 101 | + for _ in range(batch_size): |
| 102 | + batch_experiences.append(np.random.rand(6)) # state, action, reward, next_state, done |
| 103 | + return batch_experiences |
| 104 | + |
| 105 | +# Create the Gym Car2D environment |
| 106 | +env = gym.make("CarRacing-v2") |
| 107 | + |
| 108 | +# Create the PPO agent and priority network |
| 109 | +agent = PPOAgent(state_dim=env.observation_space.shape[0], action_dim=env.action_space.shape[0]) |
| 110 | +priority_network = PriorityNetwork(state_dim=env.observation_space.shape[0], action_dim=env.action_space.shape[0]) |
| 111 | + |
| 112 | +# Create the PPO trainer |
| 113 | +trainer = PPOTrainer(agent, priority_network, gamma=0.99, lambda_=0.95, epsilon=0.1, c1=0.5, c2=0.01) |
| 114 | + |
| 115 | +# Train the PPO agent |
| 116 | +trainer.train(batch_size=32, epochs=1000) |
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