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DDPG.py
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DDPG.py
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import copy
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# Implementation of Deep Deterministic Policy Gradients (DDPG)
# Paper: https://arxiv.org/abs/1509.02971
# [Not the implementation used in the TD3 paper]
class Actor(nn.Module):
def __init__(self, state_dim, action_dim, max_action):
super(Actor, self).__init__()
self.l1 = nn.Linear(state_dim, 400)
self.l2 = nn.Linear(400, 300)
self.l3 = nn.Linear(300, action_dim)
self.max_action = max_action
def forward(self, state):
a = F.relu(self.l1(state))
a = F.relu(self.l2(a))
return self.max_action * torch.tanh(self.l3(a))
class Critic(nn.Module):
def __init__(self, state_dim, action_dim):
super(Critic, self).__init__()
self.l1 = nn.Linear(state_dim, 400)
self.l2 = nn.Linear(400 + action_dim, 300)
self.l3 = nn.Linear(300, 1)
def forward(self, state, action):
q = F.relu(self.l1(state))
q = F.relu(self.l2(torch.cat([q, action], 1)))
return self.l3(q)
class DDPG(object):
def __init__(self, state_dim, action_dim, max_action, discount=0.99, tau=0.001):
self.actor = Actor(state_dim, action_dim, max_action).to(device)
self.actor_target = copy.deepcopy(self.actor)
self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=1e-4)
self.critic = Critic(state_dim, action_dim).to(device)
self.critic_target = copy.deepcopy(self.critic)
self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), weight_decay=1e-2)
self.discount = discount
self.tau = tau
def select_action(self, state):
state = torch.FloatTensor(state.reshape(1, -1)).to(device)
return self.actor(state).cpu().data.numpy().flatten()
def train(self, replay_buffer, batch_size=64):
# Sample replay buffer
state, action, next_state, reward, not_done = replay_buffer.sample(batch_size)
# Compute the target Q value
target_Q = self.critic_target(next_state, self.actor_target(next_state))
target_Q = reward + (not_done * self.discount * target_Q).detach()
# Get current Q estimate
current_Q = self.critic(state, action)
# Compute critic loss
critic_loss = F.mse_loss(current_Q, target_Q)
# Optimize the critic
self.critic_optimizer.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
# Compute actor loss
actor_loss = -self.critic(state, self.actor(state)).mean()
# Optimize the actor
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
# Update the frozen target models
for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()):
target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)
for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):
target_param.data.copy_(self.tau * param.data + (1 - self.tau) * target_param.data)
def save(self, filename):
torch.save(self.critic.state_dict(), filename + "_critic")
torch.save(self.critic_optimizer.state_dict(), filename + "_critic_optimizer")
torch.save(self.actor.state_dict(), filename + "_actor")
torch.save(self.actor_optimizer.state_dict(), filename + "_actor_optimizer")
def load(self, filename):
self.critic.load_state_dict(torch.load(filename + "_critic"))
self.critic_optimizer.load_state_dict(torch.load(filename + "_critic_optimizer"))
self.critic_target = copy.deepcopy(self.critic)
self.actor.load_state_dict(torch.load(filename + "_actor"))
self.actor_optimizer.load_state_dict(torch.load(filename + "_actor_optimizer"))
self.actor_target = copy.deepcopy(self.actor)