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DDPG.py

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+import numpy as np
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+import torch.nn.functional as F
+
+import utils
+
+
+# Implementation of Deep Deterministic Policy Gradients (DDPG)
+# Paper: https://arxiv.org/abs/1509.02971
+# [Not the implementation used in the TD3 paper]
+
+
+def var(tensor, volatile=False):
+	if torch.cuda.is_available():
+		return Variable(tensor, volatile=volatile).cuda()
+	else:
+		return Variable(tensor, volatile=volatile)
+
+
+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, x):
+		x = F.relu(self.l1(x))
+		x = F.relu(self.l2(x))
+		x = self.max_action * F.tanh(self.l3(x)) 
+		return x 
+
+
+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, x, u):
+		x = F.relu(self.l1(x))
+		x = F.relu(self.l2(torch.cat([x, u], 1)))
+		x = self.l3(x)
+		return x 
+
+
+class DDPG(object):
+	def __init__(self, state_dim, action_dim, max_action):
+		self.actor = Actor(state_dim, action_dim, max_action)
+		self.actor_target = Actor(state_dim, action_dim, max_action)
+		self.actor_target.load_state_dict(self.actor.state_dict())
+		self.actor_optimizer = torch.optim.Adam(self.actor.parameters(), lr=1e-4)
+
+		self.critic = Critic(state_dim, action_dim)
+		self.critic_target = Critic(state_dim, action_dim)
+		self.critic_target.load_state_dict(self.critic.state_dict())
+		self.critic_optimizer = torch.optim.Adam(self.critic.parameters(), weight_decay=1e-2)		
+
+		if torch.cuda.is_available():
+			self.actor = self.actor.cuda()
+			self.actor_target = self.actor_target.cuda()
+			self.critic = self.critic.cuda()
+			self.critic_target = self.critic_target.cuda()
+
+		self.criterion = nn.MSELoss()
+		self.state_dim = state_dim
+
+
+	def select_action(self, state):
+		state = var(torch.FloatTensor(state.reshape(-1, self.state_dim)), volatile=True)
+		return self.actor(state).cpu().data.numpy().flatten()
+
+
+	def train(self, replay_buffer, iterations, batch_size=64, discount=0.99, tau=0.001):
+
+		for it in range(iterations):
+
+			# Sample replay buffer 
+			x, y, u, r, d = replay_buffer.sample(batch_size)
+			state = var(torch.FloatTensor(x))
+			action = var(torch.FloatTensor(u))
+			next_state = var(torch.FloatTensor(y), volatile=True)
+			done = var(torch.FloatTensor(1 - d))
+			reward = var(torch.FloatTensor(r))
+
+			# Q target = reward + discount * Q(next_state, pi(next_state))
+			target_Q = self.critic_target(next_state, self.actor_target(next_state))
+			target_Q.volatile = False 
+			target_Q = reward + (done * discount * target_Q)
+
+			# Get current Q estimate
+			current_Q = self.critic([state, action])
+
+			# Compute critic loss
+			critic_loss = self.criterion(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_(tau * param.data + (1 - tau) * target_param.data)
+
+			for param, target_param,  in zip(self.actor.parameters(), self.actor_target.parameters()):
+				target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)
+
+
+	def save(self, filename, directory):
+		torch.save(self.actor.state_dict(), '%s/%s_actor.pth' % (directory, filename))
+		torch.save(self.critic.state_dict(), '%s/%s_critic.pth' % (directory, filename))
+
+
+	def load(self, filename, directory):
+		self.actor.load_state_dict(torch.load('%s/%s_actor.pth' % (directory, filename)))
+		self.critic.load_state_dict(torch.load('%s/%s_critic.pth' % (directory, filename)))

TD3.py

@@ -0,0 +1,154 @@
+import numpy as np
+import torch
+import torch.nn as nn
+from torch.autograd import Variable
+import torch.nn.functional as F
+
+import utils
+
+
+# Implementation of Twin Delayed Deep Deterministic Policy Gradients (TD3)
+
+
+def var(tensor, volatile=False):
+	if torch.cuda.is_available():
+		return Variable(tensor, volatile=volatile).cuda()
+	else:
+		return Variable(tensor, volatile=volatile)
+
+
+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, x):
+		x = F.relu(self.l1(x))
+		x = F.relu(self.l2(x))
+		x = self.max_action * F.tanh(self.l3(x)) 
+		return x
+
+
+class Critic(nn.Module):
+	def __init__(self, state_dim, action_dim):
+		super(Critic, self).__init__()
+
+		# Q1 architecture
+		self.l1 = nn.Linear(state_dim + action_dim, 400)
+		self.l2 = nn.Linear(400, 300)
+		self.l3 = nn.Linear(300, 1)
+
+		# Q2 architecture
+		self.l4 = nn.Linear(state_dim + action_dim, 400)
+		self.l5 = nn.Linear(400, 300)
+		self.l6 = nn.Linear(300, 1)
+
+
+	def forward(self, x, u):
+		x1 = F.relu(self.l1(torch.cat([x, u], 1)))
+		x1 = F.relu(self.l2(x1))
+		x1 = self.l3(x1)
+
+		x2 = F.relu(self.l4(torch.cat([x, u], 1)))
+		x2 = F.relu(self.l5(x2))
+		x2 = self.l6(x2)
+
+		return x1, x2
+
+
+class TD3(object):
+	def __init__(self, state_dim, action_dim, max_action):
+		self.actor = Actor(state_dim, action_dim, max_action)
+		self.actor_target = Actor(state_dim, action_dim, max_action).cuda()
+		self.actor_target.load_state_dict(self.actor.state_dict())
+		self.actor_optimizer = torch.optim.Adam(self.actor.parameters())
+
+		self.critic = Critic(state_dim, action_dim)
+		self.critic_target = Critic(state_dim, action_dim)
+		self.critic_target.load_state_dict(self.critic.state_dict())
+		self.critic_optimizer = torch.optim.Adam(self.critic.parameters())		
+
+		if torch.cuda.is_available():
+			self.actor = self.actor.cuda()
+			self.actor_target = self.actor_target.cuda()
+			self.critic = self.critic.cuda()
+			self.critic_target = self.critic_target.cuda()
+
+		self.criterion = nn.MSELoss()
+		self.state_dim = state_dim
+		self.max_action = max_action
+
+
+	def select_action(self, state):
+		state = var(torch.FloatTensor(state.reshape(-1, self.state_dim)), volatile=True)
+		return self.actor(state).cpu().data.numpy().flatten()
+
+
+	def train(self, replay_buffer, iterations, batch_size=100, discount=0.99, tau=0.005, policy_noise=0.2, noise_clip=0.5, policy_freq=2):
+
+		for it in range(iterations):
+
+			# Sample replay buffer 
+			x, y, u, r, d = replay_buffer.sample(batch_size)
+			state = var(torch.FloatTensor(x))
+			action = var(torch.FloatTensor(u))
+			next_state = var(torch.FloatTensor(y), volatile=True)
+			done = var(torch.FloatTensor(1 - d))
+			reward = var(torch.FloatTensor(r))
+
+			# Select action according to policy and add clipped noise 
+			noise = np.clip(np.random.normal(0, policy_noise, size=(batch_size,1)), -noise_clip, noise_clip)
+			next_action = self.actor_target(next_state) + var(torch.FloatTensor(noise))
+			next_action = next_action.clamp(-self.max_action, self.max_action)
+
+			# Q target = reward + discount * min(Qi(next_state, pi(next_state)))
+			target_Q1, target_Q2 = self.critic_target(next_state, next_action)
+			target_Q = torch.min(torch.cat([target_Q1, target_Q2], 1), 1)[0].view(-1, 1)
+			target_Q.volatile = False 
+			target_Q = reward + (done * discount * target_Q)
+
+			# Get current Q estimates
+			current_Q1, current_Q2 = self.critic(state, action)
+
+			# Compute critic loss
+			critic_loss = self.criterion(current_Q1, target_Q) + self.criterion(current_Q2, target_Q) 
+
+			# Optimize the critic
+			self.critic_optimizer.zero_grad()
+			critic_loss.backward()
+			self.critic_optimizer.step()
+
+			# Delayed policy updates
+			if it % policy_freq == 0:
+
+				# Compute actor loss
+				Q1, Q2 = self.critic(state, self.actor(state)) 
+				actor_loss = -Q1.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_(tau * param.data + (1 - tau) * target_param.data)
+
+				for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):
+					target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)
+
+
+	def save(self, filename, directory):
+		torch.save(self.actor.state_dict(), '%s/%s_actor.pth' % (directory, filename))
+		torch.save(self.critic.state_dict(), '%s/%s_critic.pth' % (directory, filename))
+
+
+	def load(self, filename, directory):
+		self.actor.load_state_dict(torch.load('%s/%s_actor.pth' % (directory, filename)))
+		self.critic.load_state_dict(torch.load('%s/%s_critic.pth' % (directory, filename)))