class DDPGAGENT:
def __init__(self, state_size, action_size, random_seed):
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(random_seed)
self.epsilon = EPS
#--- actor -----#
self.actor_local = Actor(state_size, action_size,
random_seed).to(device)
self.actor_target = Actor(state_size, action_size,
random_seed).to(device)
self.actor_optimizer = optim.Adam(self.actor_local.parameters(),
lr=1e-3)
#---- critic -----#
self.critic_local = Critic(state_size, action_size,
random_seed).to(device)
self.critic_target = Critic(state_size, action_size,
random_seed).to(device)
self.critic_optimizer = optim.Adam(self.critic_local.parameters(),
lr=1e-3,
weight_decay=0)
self.noise = OUNoise(action_size, random_seed)
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE,
random_seed)
#self.timestep = 0
def step(self, state, action, reward, next_state, done, timestep):
self.memory.add_experience(state, action, reward, next_state, done)
#self.timestep = (self.timestep + 1) % UPDATE_EVERY
if len(self.memory) > BATCH_SIZE and timestep % UPDATE_EVERY == 0:
for _ in range(LEARN_NUM):
xp = self.memory.sample()
self.learn(xp, GAMMA) #GAMMA VALUE 0.99
def act(self, state, noise_accumulate=True):
state = torch.from_numpy(state).float().to(device)
self.actor_local.eval()
with torch.no_grad():
action = self.actor_local(state).cpu().data.numpy()
self.actor_local.train()
#Epsilon greedy selection
if noise_accumulate:
action += self.epsilon * self.noise.sample()
return np.clip(action, -1, 1)
def reset(self):
self.noise.reset_internal_state()
def learn(self, xp, gamma):
states, actions, rewards, next_states, dones = xp
#---configuring critic and computation of loss with help of MSE
actions_nxt = self.actor_target(next_states)
q_target_next = self.critic_target(next_states, actions_nxt)
q_target = rewards + (gamma * q_target_next * (1 - dones))
q_expected = self.critic_local(states, actions)
#MSE LOSS
critic_loss = F.mse_loss(q_expected, q_target)
self.critic_optimizer.zero_grad()
critic_loss.backward()
# Clips gradient norm of an iterable of parameters
torch.nn.utils.clip_grad_norm_(self.critic_local.parameters(), 1)
self.critic_optimizer.step()
#---configuring actor and computation of loss with help of MSE
actor_predicted = self.actor_local(states)
actor_loss = -self.critic_local(states, actor_predicted).mean()
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
self.soft_update(self.critic_local, self.critic_target, TAU)
self.soft_update(self.actor_local, self.actor_target, TAU)
self.epsilon -= 1e-6
self.noise.reset_internal_state()
def soft_update(self, local_model, target_model, tau):
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
class Agent():
def __init__(self,
state_size,
action_size,
action_sigma=0.1,
memory_size=1000000,
batch=128,
sigma=0.2,
noise_clip=0.5,
gamma=0.99,
update_frequency=2,
seed=0):
'''
TD3 Agent
:param state_size: State Dimension
:param action_size: Action dimension
:param action_sigma: standard deviation of the noise to be added to the action
:param memory_size:
:param batch:
:param sigma: Standard deviation of the noise to be added to the target function (Chapter 5.3 of TD3 Paper)
:param noise_clip: How much noise to allow
:param gamma:
:param update_frequency:
:param seed:
'''
self.state_size = state_size
self.action_size = action_size
self.action_sigma = action_sigma
self.sigma = sigma
self.noise_clip = noise_clip
self.gamma = gamma
self.update_frequency = update_frequency
self.seed = seed
self.actor = Actor(self.state_size, self.action_size).to(device)
self.critic0 = Critic(self.state_size, self.action_size).to(device)
#second Critic as described in the paper
# https: // arxiv.org / pdf / 1802.09477.pdf
self.critic1 = Critic(self.state_size, self.action_size).to(device)
self.target_actor = Actor(self.state_size, self.action_size).to(device)
self.target_critic0 = Critic(self.state_size,
self.action_size).to(device)
# second Critic as described in the paper
# https: // arxiv.org / pdf / 1802.09477.pdf
self.target_critic1 = Critic(self.state_size,
self.action_size).to(device)
self.memory = ReplayBuffer(memory_size, batch, seed=seed)
self.actor_optimizer = Adam(self.actor.parameters(), lr=ACTOR_LR)
self.critic0_optimizer = Adam(self.critic0.parameters(), lr=VALUE0_LR)
self.critic1_optimizer = Adam(self.critic1.parameters(), lr=VALUE1_LR)
self.soft_update(self.actor, self.target_actor, 1)
self.soft_update(self.critic0, self.target_critic0, 1)
self.soft_update(self.critic1, self.target_critic1, 1)
def act(self, state, epsilon=True):
state = torch.from_numpy(np.asarray(state)).float().to(device)
self.actor.eval()
with torch.no_grad():
action = self.actor.forward(state).cpu().data.numpy()
self.actor.train()
if epsilon:
#if we want to inject some noise
noise = np.random.normal(0, self.action_sigma, action.shape[0])
action += noise
return action
def update(self, step):
'''
#https: // arxiv.org / pdf / 1802.09477.pdf
the function is very similar to typical DDPG algorithm, except for
1) we have 2 critics to update
2) we take the min of the 2 values critics output
3) Has modified Target network with noise injected into it (Chapter 5.3 of the paper)
4) We delay updating the actor by certain steps
:param step: how often to update the actor
:return:
'''
state, action, reward, next_state, done = self.memory.sample()
# ---------------------------- update critic ---------------------------- #
# Get predicted next-state actions and Q values from target models
next_state_action = self.target_actor(next_state)
#sample a random noise
noise = Normal(torch.zeros(self.action_size), self.sigma).sample()
noise = torch.clamp(noise, -self.noise_clip,
self.noise_clip).to(device)
next_state_action += noise
target_Q0 = self.target_critic0(next_state, next_state_action)
target_Q1 = self.target_critic1(next_state, next_state_action)
target_Q = torch.min(target_Q0, target_Q1)
target_value = reward + self.gamma * target_Q * (1.0 - done)
expected_Q0 = self.critic0(state, action)
expected_Q1 = self.critic1(state, action)
critic_0_loss = F.mse_loss(expected_Q0, target_value.detach())
critic_1_loss = F.mse_loss(expected_Q1, target_value.detach())
self.critic0_optimizer.zero_grad()
critic_0_loss.backward()
self.critic0_optimizer.step()
self.critic1_optimizer.zero_grad()
critic_1_loss.backward()
self.critic1_optimizer.step()
# ---------------------------- update actor ---------------------------- #
# Compute actor loss
#as mentioned in the paper, we delay updating the actor network.
if step % self.update_frequency == 0:
actor_loss = self.critic0.forward(state, self.actor.forward(state))
actor_loss = -actor_loss.mean()
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
# ----------------------- update target networks ------------------- #
self.soft_update(self.critic0, self.target_critic0, TRANSFER_RATE)
self.soft_update(self.critic1, self.target_critic1, TRANSFER_RATE)
self.soft_update(self.actor, self.target_actor, TRANSFER_RATE)
def soft_update(self, local_model, target_model, tao):
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tao * local_param.data +
(1.0 - tao) * target_param.data)
def add_to_memory(self, state, action, reward, next_state, done):
self.memory.add(state, action, reward, next_state, done)
class Agent():
"""Interacts with and learns from the environment."""
def __init__(
self,
state_size=24,
action_size=2,
BATCH_SIZE=128,
BUFFER_SIZE=int(1e6),
discount_factor=1,
tau=1e-2,
noise_coefficient_start=5,
noise_coefficient_decay=0.99,
LR_ACTOR=1e-3,
LR_CRITIC=1e-3,
WEIGHT_DECAY=1e-3,
device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")):
"""
state_size (int): dimension of each state
action_size (int): dimension of each action
BATCH_SIZE (int): mini batch size
BUFFER_SIZE (int): experience storing lenght, keep it as high as possible
discount_factor (float): discount factor for calculating Q_target
tau (float): interpolation parameter for updating target network
noise_coefficient_start (float): value to be multiplied to OUNoise sample
noise_coefficient_decay (float): exponential decay factor for value to be multiplied to OUNoise sample
LR_ACTOR (float): learning rate for actor network
LR_CRITIC (float): learning rate for critic network
WEIGHT_DECAY (float): Weight decay for critic network optimizer
device : "cuda:0" if torch.cuda.is_available() else "cpu"
"""
self.state_size = state_size
print(device)
self.action_size = action_size
self.BATCH_SIZE = BATCH_SIZE
self.BUFFER_SIZE = BUFFER_SIZE
self.discount_factor = discount_factor
self.tau = tau
self.noise_coefficient = noise_coefficient_start
self.noise_coefficient_decay = noise_coefficient_decay
self.steps_completed = 0
self.device = device
# Actor Network (w/ Target Network)
self.actor_local = Actor(state_size, action_size).to(self.device)
self.actor_target = Actor(state_size, action_size).to(self.device)
self.actor_optimizer = optim.Adam(self.actor_local.parameters(),
lr=LR_ACTOR)
# Critic Network (w/ Target Network)
self.critic_local = Critic(state_size, action_size).to(self.device)
self.critic_target = Critic(state_size, action_size).to(self.device)
self.critic_optimizer = optim.Adam(self.critic_local.parameters(),
lr=LR_CRITIC,
weight_decay=WEIGHT_DECAY)
# Noise process
self.noise = OUNoise((1, action_size))
# Replay memory
self.memory = ReplayBuffer(action_size, self.BUFFER_SIZE,
self.BATCH_SIZE)
def step(self, state, action, reward, next_state, done, agent_number):
"""Save experience in replay memory, and use random sample from buffer to learn."""
self.memory.add(state, action, reward, next_state, done)
self.steps_completed += 1
# If number of memory data > Batch_Size then learn
if len(self.memory) > self.BATCH_SIZE:
experiences = self.memory.sample(self.device)
self.learn(experiences, self.discount_factor, agent_number)
def act(self, states, add_noise):
"""Returns actions for given state as per current policy."""
states = torch.from_numpy(states).float().to(self.device)
actions = np.zeros((1, self.action_size)) # shape will be (1,2)
self.actor_local.eval()
with torch.no_grad():
actions[0, :] = self.actor_local(states).cpu().data.numpy()
self.actor_local.train()
if add_noise:
actions += self.noise_coefficient * self.noise.sample()
return np.clip(actions, -1, 1)
def reset(self):
self.noise.reset()
def learn(self, experiences, discount_factor, agent_number):
"""Update policy and value parameters using given batch of experience tuples.
Q_targets = r + γ * critic_target(next_state, actor_target(next_state))
where:
actor_target(state) -> action
critic_target(state, action) -> Q-value
Params
======
experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
discount_factor (float): discount factor
"""
states, actions, rewards, next_states, dones = experiences
# ---------------------------- update critic ---------------------------- #
# Get predicted next-state actions and Q values from target models
actions_next = self.actor_target(next_states)
# It is basically taking action of both the agents, so if agent_number=0 then we will have to concatenate agent0 action(currently actions_next) and agent1 action(currently actions[:,2:])
if agent_number == 0:
actions_next = torch.cat((actions_next, actions[:, 2:]), dim=1)
else:
actions_next = torch.cat((actions[:, :2], actions_next), dim=1)
Q_targets_next = self.critic_target(next_states, actions_next)
# Compute Q targets for current states (y_i)
Q_targets = rewards + (discount_factor * Q_targets_next * (1 - dones))
# Compute critic loss
Q_expected = self.critic_local(states, actions)
critic_loss = F.mse_loss(Q_expected, Q_targets)
# Minimize the loss
self.critic_optimizer.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
# ---------------------------- update actor ---------------------------- #
# Compute actor loss
actions_pred = self.actor_local(states)
if agent_number == 0:
actions_pred = torch.cat((actions_pred, actions[:, 2:]), dim=1)
else:
actions_pred = torch.cat((actions[:, :2], actions_pred), dim=1)
actor_loss = -self.critic_local(states, actions_pred).mean()
# Minimize the loss
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
# ----------------------- update target networks ----------------------- #
self.soft_update(self.critic_local, self.critic_target)
self.soft_update(self.actor_local, self.actor_target)
# Update noise_coefficient value
# self.noise_coefficient = self.noise_coefficient*self.noise_coefficient_decay
self.noise_coefficient = max(
self.noise_coefficient - (1 / self.noise_coefficient_decay), 0)
# print(self.steps_completed,': ',self.noise_coefficient)
def soft_update(self, local_model, target_model):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
local_model: PyTorch model (weights will be copied from)
target_model: PyTorch model (weights will be copied to)
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(self.tau * local_param.data +
(1.0 - self.tau) * target_param.data)
class Agent():
""" Interacts with and learns from the environment """
def __init__(self, state_size, action_size, num_agents, seed):
"""
Initialize an Agent object
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
num_agents (int): number of agents
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.num_agents = num_agents
self.seed = random.seed(seed)
self.eps = eps_start
self.t_step = 0
# Actor Network (with Target Network)
self.actor_local = Actor(state_size, action_size, seed).to(device)
self.actor_target = Actor(state_size, action_size, seed).to(device)
self.actor_optimizer = optim.Adam(self.actor_local.parameters(),
lr=LR_ACTOR)
# Critic Network (with Target Network)
self.critic_local = Critic(state_size, action_size, seed).to(device)
self.critic_target = Critic(state_size, action_size, seed).to(device)
self.critic_optimizer = optim.Adam(self.critic_local.parameters(),
lr=LR_CRITIC,
weight_decay=WEIGHT_DECAY)
# Noise process
self.noise = OUNoise((num_agents, action_size), seed)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
def step(self, state, action, reward, next_state, done, agent_number):
""" Save experience in replay memory, and use random sample from buffer to learn """
self.t_step += 1
# Save experience / reward
self.memory.add(state, action, reward, next_state, done)
# Learn, if enough samples are available in memory and at interval settings
if len(self.memory) > BATCH_SIZE:
if self.t_step % UPDATE_EVERY == 0:
for _ in range(N_UPDATES):
experiences = self.memory.sample()
self.learn(experiences, GAMMA, agent_number)
def act(self, states, add_noise):
""" Returns actions for given state as per current policy """
states = torch.from_numpy(states).float().to(device)
actions = np.zeros((self.num_agents, self.action_size))
self.actor_local.eval()
with torch.no_grad():
for agent_num, state in enumerate(states):
action = self.actor_local(state).cpu().data.numpy()
actions[agent_num, :] = action
self.actor_local.train()
if add_noise:
actions += self.eps * self.noise.sample()
return np.clip(actions, -1, 1)
def reset(self):
self.noise.reset()
def learn(self, experiences, gamma, agent_number):
"""
Update policy and value parameters using given batch of experience tuples
Q_targets = r + γ * critic_target(next_state, actor_target(next_state))
where:
actor_target(state) -> action
critic_target(state, action) -> Q-value
Params
======
experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = experiences
# ---------------------------- update critic ---------------------------- #
# Get predicted next-state actions and Q values from target models
actions_next = self.actor_target(next_states)
if agent_number == 0:
actions_next = torch.cat((actions_next, actions[:, 2:]), dim=1)
else:
actions_next = torch.cat((actions[:, :2], actions_next), dim=1)
Q_targets_next = self.critic_target(next_states, actions_next)
# Compute Q targets for current states (y_i)
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
# Compute critic loss
Q_expected = self.critic_local(states, actions)
critic_loss = F.mse_loss(Q_expected, Q_targets)
# Minimize the loss
self.critic_optimizer.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
# ---------------------------- update actor ---------------------------- #
# Compute actor loss
actions_pred = self.actor_local(states)
if agent_number == 0:
actions_pred = torch.cat((actions_pred, actions[:, 2:]), dim=1)
else:
actions_pred = torch.cat((actions[:, :2], actions_pred), dim=1)
actor_loss = -self.critic_local(states, actions_pred).mean()
# Minimize the loss
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
# ----------------------- update target networks ----------------------- #
self.soft_update(self.critic_local, self.critic_target, TAU)
self.soft_update(self.actor_local, self.actor_target, TAU)
# Update epsilon noise value
self.eps = self.eps - (1 / eps_decay)
if self.eps < eps_end:
self.eps = eps_end
def soft_update(self, local_model, target_model, tau):
"""
Soft update model parameters
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model: PyTorch model (weights will be copied from)
target_model: PyTorch model (weights will be copied to)
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, n, state_size, action_size, random_seed, params):
"""Initialize an Agent object.
Params
======
n (int): number of agents in env
state_size (int): dimension of each state
action_size (int): dimension of each action
random_seed (int): random seed
params (dict): dictionary with hyperparameters name-value pairs
"""
self.n = n
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(random_seed)
self.BUFFER_SIZE = params["BUFFER_SIZE"]
self.BATCH_SIZE = params["BATCH_SIZE"]
self.GAMMA = params["GAMMA"]
self.TAU = params["TAU"]
self.LR_ACTOR = params["LR_ACTOR"]
self.LR_CRITIC = params["LR_CRITIC"]
self.WEIGHT_DECAY = params["WEIGHT_DECAY"]
self.N_UPDATES = params["N_UPDATES"]
self.UPDATE_STEP = params["UPDATE_STEP"]
# Actor Network (w/ Target Network)
self.actor_local = Actor(state_size, action_size,
random_seed).to(device)
self.actor_target = Actor(state_size, action_size,
random_seed).to(device)
self.actor_optimizer = optim.Adam(self.actor_local.parameters(),
lr=self.LR_ACTOR)
# Critic Network (w/ Target Network)
self.critic_local = Critic(state_size, action_size,
random_seed).to(device)
self.critic_target = Critic(state_size, action_size,
random_seed).to(device)
self.critic_optimizer = optim.Adam(self.critic_local.parameters(),
lr=self.LR_CRITIC,
weight_decay=self.WEIGHT_DECAY)
# Noise process
self.noise = OUNoise(self.n, action_size, random_seed)
# Replay memory
self.memory = ReplayBuffer(action_size, self.BUFFER_SIZE,
self.BATCH_SIZE, random_seed)
#Count timesteps
self.timestep = 0
def step(self, state, action, reward, next_state, done):
"""Save experience in replay memory, and use random sample from buffer to learn."""
# Save experience / reward
for i in range(self.n):
self.memory.add(state[i, :], action[i, :], reward[i],
next_state[i, :], done[i])
self.timestep += 1
# Learn, if enough samples are available in memory
if self.timestep % self.UPDATE_STEP == 0 and len(
self.memory) > self.BATCH_SIZE:
for _ in range(self.N_UPDATES):
experiences = self.memory.sample()
self.learn(experiences, self.GAMMA)
def act(self, state, add_noise=True):
"""Returns actions for given state as per current policy."""
state = torch.from_numpy(state).float().to(device)
self.actor_local.eval()
with torch.no_grad():
action = self.actor_local(state).cpu().data.numpy()
self.actor_local.train()
if add_noise:
action += self.noise.sample()
return np.clip(action, -1, 1)
def reset(self):
self.noise.reset()
def learn(self, experiences, gamma):
"""Update policy and value parameters using given batch of experience tuples.
Q_targets = r + γ * critic_target(next_state, actor_target(next_state))
where:
actor_target(state) -> action
critic_target(state, action) -> Q-value
Params
======
experiences (Tuple[torch.Tensor]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
states, actions, rewards, next_states, dones = experiences
# ---------------------------- update critic ---------------------------- #
# Get predicted next-state actions and Q values from target models
actions_next = self.actor_target(next_states)
Q_targets_next = self.critic_target(next_states, actions_next)
# Compute Q targets for current states (y_i)
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
# Compute critic loss
Q_expected = self.critic_local(states, actions)
critic_loss = F.mse_loss(Q_expected, Q_targets)
# Minimize the loss
self.critic_optimizer.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
# ---------------------------- update actor ---------------------------- #
# Compute actor loss
actions_pred = self.actor_local(states)
actor_loss = -self.critic_local(states, actions_pred).mean()
# Minimize the loss
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
# ----------------------- update target networks ----------------------- #
self.soft_update(self.critic_local, self.critic_target, self.TAU)
self.soft_update(self.actor_local, self.actor_target, self.TAU)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model: PyTorch model (weights will be copied from)
target_model: PyTorch model (weights will be copied to)
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, random_seed, num_agents):
"""Initialize an Agent object.
"""
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(random_seed)
# Actor Network (w/ Target Network)
self.actor_local = Actor(state_size, action_size,
random_seed).to(device)
self.actor_target = Actor(state_size, action_size,
random_seed).to(device)
self.actor_optimizer = optim.Adam(self.actor_local.parameters(),
lr=LR_ACTOR)
# Critic Network (w/ Target Network)
self.critic_local = Critic(state_size, action_size,
random_seed).to(device)
self.critic_target = Critic(state_size, action_size,
random_seed).to(device)
self.critic_optimizer = optim.Adam(self.critic_local.parameters(),
lr=LR_CRITIC,
weight_decay=WEIGHT_DECAY)
# Noise process
self.noise = OUNoise(action_size, random_seed, sigma=0.1)
# Replay buffer
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE,
random_seed)
self.num_agents = num_agents
def step(self, state, action, reward, next_state, done):
"""Save experience in replay memory, and use random sample from buffer to learn."""
# Save experience / reward
#self.memory.add(state, action, reward, next_state, done)
for i in range(self.num_agents):
self.memory.add(state[i], action[i], reward[i], next_state[i],
done)
# Learn, if enough samples are available in memory
if len(self.memory) > BATCH_SIZE:
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def act(self, state, add_noise=True):
"""Returns actions for given state as per current policy."""
state = torch.from_numpy(state).float().to(device)
self.actor_local.eval()
with torch.no_grad():
action = self.actor_local(state).cpu().data.numpy()
self.actor_local.train()
if add_noise:
action += self.noise.sample()
return np.clip(action, -1, 1)
def reset(self):
self.noise.reset()
def learn(self, experiences, gamma):
states, actions, rewards, next_states, dones = experiences
# update critic
actions_next = self.actor_target(next_states)
Q_targets_next = self.critic_target(next_states, actions_next)
Q_targets = rewards + (gamma * Q_targets_next * (1 - dones))
Q_expected = self.critic_local(states, actions)
critic_loss = F.mse_loss(Q_expected, Q_targets)
self.critic_optimizer.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
# update actor
actions_pred = self.actor_local(states)
actor_loss = -self.critic_local(states, actions_pred).mean()
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
#update target networks
self.soft_update(self.critic_local, self.critic_target, TAU)
self.soft_update(self.actor_local, self.actor_target, TAU)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model: PyTorch model (weights will be copied from)
target_model: PyTorch model (weights will be copied to)
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
if __name__ == '__main__':
# load the model param
model_path = 'saved_models/%s/%s/model.pt' % (args.env, args.her_strat)
o_mean, o_std, g_mean, g_std, model = torch.load(model_path, map_location=lambda storage, loc: storage)
# create the environment
env = gym.make(args.env)
# get the env param
obs = env.reset()
# get the environment params
act_dim = env.action_space.shape[0]
env_dim = obs['observation'].shape[0] + obs['desired_goal'].shape[0]
act_range = env.action_space.high[0]
# create the actor network
actor_network = Actor(env_dim, act_dim, act_range)
actor_network.load_state_dict(model)
actor_network.eval()
for i in range(DEMO_LENGHT):
observation = env.reset()
# start to do the demo
obs = observation['observation']
g = observation['desired_goal']
for t in range(env._max_episode_steps):
env.render()
inputs = process_inputs(obs, g, o_mean, o_std, g_mean, g_std)
with torch.no_grad():
pi = actor_network(inputs)
action = pi.detach().numpy().squeeze()
# put actions into the environment
observation_new, reward, done, info = env.step(action)
if info['is_success']:
break
