class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, n_agents, state_size, action_size, random_seed):
"""Initialize an Agent object.
Params
======
n_agents (int): number of agents
state_size (int): dimension of each state
action_size (int): dimension of each action
random_seed (int): random seed
"""
self.n_agents = n_agents
self.state_size = state_size
self.action_size = action_size
self.seed = np.random.seed(random_seed)
random.seed(random_seed)
# Actor Network (w/ Target Network)
# self.actor_local = Actor(state_size, action_size, seed=random_seed, leak=LEAKINESS).to(device)
# self.actor_target = Actor(state_size, action_size, seed=random_seed, leak=LEAKINESS).to(device)
self.actor_local = Actor(state_size, action_size,
seed=random_seed).to(device)
self.actor_target = Actor(state_size, action_size,
seed=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, seed=random_seed, leak=LEAKINESS).to(device)
# self.critic_target = Critic(state_size, action_size, seed=random_seed, leak=LEAKINESS).to(device)
self.critic_local = Critic(state_size, action_size,
seed=random_seed).to(device)
self.critic_target = Critic(state_size, action_size,
seed=random_seed).to(device)
self.critic_optimizer = optim.Adam(self.critic_local.parameters(),
lr=LR_CRITIC,
weight_decay=WEIGHT_DECAY)
# self.critic_optimizer = optim.Adam(self.critic_local.parameters(), lr=LR_CRITIC)
# initialize targets same as original networks
self.hard_update(self.actor_target, self.actor_local)
self.hard_update(self.critic_target, self.critic_local)
# Noise process
# self.noise = OUNoise((n_agents, action_size), random_seed)
self.noise = OUNoise(action_size, random_seed)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE,
random_seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def step(self, states, actions, rewards, next_states, dones):
"""Save experience in replay memory, and use random sample from buffer to learn."""
# Save experience / reward
for state, action, reward, next_state, done in zip(
states, actions, rewards, next_states, dones):
self.memory.add(state, action, reward, next_state, done)
# self.memory.add(states, actions, rewards, next_states, dones)
# Learn, if enough samples are available in memory
self.t_step = self.t_step + 1
if (len(self.memory) > BATCH_SIZE) and (self.t_step % UPDATE_EVERY
== 0):
# experiences = self.memory.sample()
# self.learn(experiences, GAMMA)
for _ in range(10):
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):
"""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)
# Compute critic loss
critic_loss = F.mse_loss(Q_expected, Q_targets)
# Minimize the loss
self.critic_optimizer.zero_grad()
critic_loss.backward()
torch.nn.utils.clip_grad_norm_(
self.critic_local.parameters(),
1) # Added because I got converge problems
# then I found in adaptationio's solution (https://github.com/adaptationio/DDPG-Continuous-Control), he added this.
# Here to see the purpose of doing so: https://discuss.pytorch.org/t/about-torch-nn-utils-clip-grad-norm/13873
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()
torch.nn.utils.clip_grad_norm_(
self.actor_local.parameters(),
1) # Added because I got converge problems
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)
def hard_update(self, target, source):
"""
Copy network parameters from source to target
Inputs:
target (torch.nn.Module): Net to copy parameters to
source (torch.nn.Module): Net whose parameters to copy
"""
for target_param, param in zip(target.parameters(),
source.parameters()):
target_param.data.copy_(param.data)
class MADDPGAgent():
"""Interacts with and learns from the environment."""
def __init__(self,
state_size,
action_size,
n_agents,
current_agent,
random_seed=0):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
n_agents (int): number of agents
current_agent (int): index of current agent
random_seed (int): random seed
"""
self.n_agents = n_agents
self.current_agent = current_agent
self.state_size = state_size
self.action_size = action_size
self.seed = np.random.seed(random_seed)
random.seed(random_seed)
# Actor Network (w/ Target Network)
# self.actor_local = Actor(state_size, action_size, seed=random_seed, leak=LEAKINESS, use_bn=False).to(device)
# self.actor_target = Actor(state_size, action_size, seed=random_seed, leak=LEAKINESS, use_bn=False).to(device)
self.actor_local = Actor(state_size,
action_size,
seed=random_seed,
use_bn=False).to(device)
self.actor_target = Actor(state_size,
action_size,
seed=random_seed,
use_bn=False).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 * n_agents, action_size * n_agents,
# seed=random_seed, leak=LEAKINESS, use_bn=False).to(device)
# self.critic_target = Critic(self.state_size * n_agents, self.action_size * n_agents,
# seed=random_seed, leak=LEAKINESS, use_bn=False).to(device)
self.critic_local = Critic(state_size * n_agents,
action_size * n_agents,
seed=random_seed,
use_bn=False).to(device)
self.critic_target = Critic(self.state_size * n_agents,
self.action_size * n_agents,
seed=random_seed,
use_bn=False).to(device)
self.critic_optimizer = optim.Adam(self.critic_local.parameters(),
lr=LR_CRITIC,
weight_decay=WEIGHT_DECAY)
# self.critic_optimizer = optim.Adam(self.critic_local.parameters(), lr=LR_CRITIC)
# Noise process
self.noise = OUNoise(action_size, random_seed)
# self.noise = OUNoise((n_agents, action_size), random_seed)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE,
random_seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def act(self, state, add_noise=True):
"""Returns actions for given state as per current policy."""
state = torch.from_numpy(state).float().to(device)
# Run inference in eval mode
self.actor_local.eval()
with torch.no_grad():
action = self.actor_local(state).cpu().data.numpy()
self.actor_local.train()
# add noise if true
if add_noise:
action += self.noise.sample()
return np.clip(action, -1, 1)
def reset(self):
self.noise.reset()
def learn(self, agents, 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
======
agents (Agent class): all agents object
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 = torch.zeros(
(len(states), self.n_agents, self.action_size)).to(device)
for i, agent in enumerate(agents):
actions_next[:, i] = agent.actor_target(states[:, i, :])
# print('\nactions_next:', actions_next.size(), '\nnext_states:', next_states.size())
# # Flatten state and action
# # e.g from state (100,2,24) --> (100, 48)
critic_states = flatten(next_states)
actions_next = flatten(actions_next)
# print('after flatten:\nactions_next:', actions_next.size(), '\nnext_states:', critic_states.size())
# calculate target and expected
Q_targets_next = self.critic_target(critic_states, actions_next)
# Q_targets_next = self.critic_target(next_states, actions_next)
# print('Q_targets_next:', Q_targets_next.size())
Q_targets = rewards[:, self.current_agent, :] + (
gamma * Q_targets_next * (1 - dones[:, self.current_agent, :]))
# print('Q_targets', Q_targets.size())
# print('\nactions:', actions.size(), '\nstates:', states.size())
# print('after flatten:\nactions:', flatten(actions).size(), '\nstates:', flatten(states).size())
Q_expected = self.critic_local(flatten(states), flatten(actions))
# Q_expected = self.critic_local(states, actions)
# # Compute critic loss, use mse loss
critic_loss = F.mse_loss(Q_expected, Q_targets)
# critic_loss_value = critic_loss.item()
self.critic_optimizer.zero_grad()
critic_loss.backward()
torch.nn.utils.clip_grad_norm_(
self.critic_local.parameters(),
1) # Added because I got converge problems
# then I found in adaptationio's solution (https://github.com/adaptationio/DDPG-Continuous-Control), he added this.
# Here to see the purpose of doing so: https://discuss.pytorch.org/t/about-torch-nn-utils-clip-grad-norm/13873
# for param in self.critic_local.parameters():
# param.grad.data.clamp_(-1, 1)
self.critic_optimizer.step()
# ---------------------------- update actor ---------------------------- #
# Update the predicted action of current agent
actions_pred = torch.zeros(
(len(states), self.n_agents, self.action_size)).to(device)
actions_pred.data.copy_(actions.data)
actions_pred[:, self.current_agent] = self.actor_local(
states[:, self.current_agent])
actor_loss = -self.critic_local(flatten(states),
flatten(actions_pred)).mean()
# actor_loss = -self.critic_local(states, actions_pred).mean()
# Minimize the loss
self.actor_optimizer.zero_grad()
actor_loss.backward()
torch.nn.utils.clip_grad_norm_(
self.actor_local.parameters(),
1) # Added because I got converge problems
# for param in self.critic_local.parameters():
# param.grad.data.clamp_(-1, 1)
self.actor_optimizer.step()
# ----------------------- update target networks ----------------------- #
if self.t_step == 0:
# One time only, start local and target with same parameters
self._copy_weights(self.critic_local, self.critic_target)
self._copy_weights(self.actor_local, self.actor_target)
else:
self.soft_update(self.critic_local, self.critic_target, TAU)
self.soft_update(self.actor_local, self.actor_target, TAU)
self.t_step += 1
def _copy_weights(self, source_network, target_network):
"""Copy source network weights to target"""
for target_param, source_param in zip(target_network.parameters(),
source_network.parameters()):
target_param.data.copy_(source_param.data)
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_agents,
state_size,
action_size,
random_seed,
prioritized_reply=False):
"""Initialize an Agent object.
Params
======
n_agents (int): number of agents
state_size (int): dimension of each state
action_size (int): dimension of each action
random_seed (int): random seed
prioritized_reply (bool): True or False for using prioritized reply buffer, default is False
"""
self.n_agents = n_agents
self.state_size = state_size
self.action_size = action_size
self.seed = np.random.seed(random_seed)
random.seed(random_seed)
self.prioritized_reply = prioritized_reply
# Actor Network (w/ Target Network)
self.actor_local = Actor(state_size,
action_size,
seed=random_seed,
leak=LEAKINESS).to(device)
self.actor_target = Actor(state_size,
action_size,
seed=random_seed,
leak=LEAKINESS).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,
seed=random_seed,
leak=LEAKINESS).to(device)
self.critic_target = Critic(state_size,
action_size,
seed=random_seed,
leak=LEAKINESS).to(device)
self.critic_optimizer = optim.Adam(self.critic_local.parameters(),
lr=LR_CRITIC)
# Noise process
self.noise = OUNoise((20, action_size), random_seed)
# Replay memory
if self.prioritized_reply:
self.memory = PreReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE,
random_seed, ALPHA)
# Initialize learning step for updating beta
self.learn_step = 0
else:
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE,
random_seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = 0
def step(self, states, actions, rewards, next_states, dones):
"""Save experience in replay memory, and use random sample from buffer to learn."""
# Save experience / reward
self.t_step = self.t_step + 1
for state, action, reward, next_state, done in zip(
states, actions, rewards, next_states, dones):
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps
# If enough samples are available in memory, get random subset and learn
if (len(self.memory) > BATCH_SIZE) and (self.t_step % UPDATE_EVERY
== 0):
if self.prioritized_reply:
experiences = self.memory.sample()
self.learn(experiences, GAMMA, BETA)
else:
for _ in range(10):
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, beta=None):
"""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
beta (float): reliance of importance sampling weight on prioritization
"""
if self.prioritized_reply:
# Beta will reach 1 after 25,000 training steps (~325 episodes)
b = min(1.0, beta + self.learn_step * (1.0 - beta) / 25000)
self.learn_step += 1
states, actions, rewards, next_states, dones, probabilities, indices = experiences
else:
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)
if self.prioritized_reply:
# Compute and update new priorities
new_priorities = (abs(Q_expected - Q_targets) + 0.2).detach()
self.memory.update_priority(new_priorities, indices)
# Compute and apply importance sampling weights to TD Errors
ISweights = (((1 / len(self.memory)) * (1 / probabilities))**b)
max_ISweight = torch.max(ISweights)
ISweights /= max_ISweight
Q_targets *= ISweights
Q_expected *= ISweights
# Compute critic loss
critic_loss = F.mse_loss(Q_expected, Q_targets)
# Minimize the loss
self.critic_optimizer.zero_grad()
critic_loss.backward()
torch.nn.utils.clip_grad_norm_(
self.critic_local.parameters(),
1) # Added because I got converge problems
# then I found in adaptationio's solution (https://github.com/adaptationio/DDPG-Continuous-Control), he added this.
# Here to see the purpose of doing so: https://discuss.pytorch.org/t/about-torch-nn-utils-clip-grad-norm/13873
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, 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)