def main():
ou = OrnsteinUhlenbeck(mu=torch.zeros(1), sigma=0.05 * torch.ones(1))
xs = list(range(100000))
ys = []
for x in xs:
y = ou()
ys.append(y.data)
plt.plot(xs, ys)
plt.show()
def __init__(self, state_size, action_size, fc1_units, fc2_units,
num_agents):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
random_seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = torch.manual_seed(SEED)
self.num_agents = num_agents
# Actor Network (w/ Target Network)
self.actor_local = Actor(state_size, action_size, fc1_units,
fc2_units).to(device)
self.actor_target = Actor(state_size, action_size, fc1_units,
fc2_units).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, fc1_units,
fc2_units).to(device)
self.critic_target = Critic(state_size, action_size, fc1_units,
fc2_units).to(device)
self.critic_optimizer = optim.Adam(self.critic_local.parameters(),
lr=LR_CRITIC,
weight_decay=WEIGHT_DECAY)
# Noise process
self.noise = OrnsteinUhlenbeck((num_agents, action_size), SEED)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, SEED,
device)
def __init__(self,
env,
log_dir,
gamma=0.99,
batch_size=64,
sigma=0.2,
batch_norm=True,
merge_layer=2,
buffer_size=int(1e6),
buffer_min=int(1e4),
tau=1e-3,
Q_wd=1e-2,
num_episodes=1000):
self.s_dim = env.reset().shape[0]
# self.a_dim = env.action_space.shape[0]
self.a_dim = env.action_space2.shape[0]
# self.a_dim = 1
self.env = env
# self.mu = Actor(self.s_dim, self.a_dim, env.action_space, batch_norm=batch_norm)
self.mu = Actor(self.s_dim,
self.a_dim,
env.action_space2,
batch_norm=batch_norm)
self.Q = Critic(self.s_dim,
self.a_dim,
batch_norm=batch_norm,
merge_layer=merge_layer)
self.targ_mu = copy.deepcopy(self.mu).eval()
self.targ_Q = copy.deepcopy(self.Q).eval()
self.noise = OrnsteinUhlenbeck(mu=torch.zeros(self.a_dim),
sigma=sigma * torch.ones(self.a_dim))
self.buffer = Buffer(buffer_size, self.s_dim, self.a_dim)
self.buffer_min = buffer_min
self.mse_fn = torch.nn.MSELoss()
self.mu_optimizer = torch.optim.Adam(self.mu.parameters(), lr=1e-4)
self.Q_optimizer = torch.optim.Adam(self.Q.parameters(),
lr=1e-3,
weight_decay=Q_wd)
self.gamma = gamma
self.batch_size = batch_size
self.num_episodes = num_episodes
self.tau = tau
self.log_dir = log_dir
self.fill_buffer()
class Agent():
""" Interacts with and learns from the environment. """
def __init__(self, state_size, action_size, fc1_units, fc2_units,
num_agents):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
random_seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = torch.manual_seed(SEED)
self.num_agents = num_agents
# Actor Network (w/ Target Network)
self.actor_local = Actor(state_size, action_size, fc1_units,
fc2_units).to(device)
self.actor_target = Actor(state_size, action_size, fc1_units,
fc2_units).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, fc1_units,
fc2_units).to(device)
self.critic_target = Critic(state_size, action_size, fc1_units,
fc2_units).to(device)
self.critic_optimizer = optim.Adam(self.critic_local.parameters(),
lr=LR_CRITIC,
weight_decay=WEIGHT_DECAY)
# Noise process
self.noise = OrnsteinUhlenbeck((num_agents, action_size), SEED)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, SEED,
device)
def step(self, step, state, action, reward, next_state, done,
agent_number):
"""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)
# Learn, if enough samples are available in memory and if step > 10
if (len(self.memory) > BATCH_SIZE) and (step % N_TIME_STEPS == 0):
for n in range(N_LEARN_UPDATES):
experiences = self.memory.sample()
self.learn(experiences, GAMMA, agent_number)
def act(self, states, add_noise=True):
"""Returns actions for both agents as per current policy, given their respective states."""
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():
# get action for each agent and concatenate them
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.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)
# Construct next actions vector relative to the agent
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)
# Compute Q targets for current states (y_i)
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()
torch.nn.utils.clip_grad_norm_(self.critic_local.parameters(), 1)
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)
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, fc1_units, fc2_units):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
random_seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.seed = torch.manual_seed(SEED)
# Actor Network (w/ Target Network)
self.actor_local = Actor(state_size, action_size, fc1_units,
fc2_units).to(device)
self.actor_target = Actor(state_size, action_size, fc1_units,
fc2_units).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, fc1_units,
fc2_units).to(device)
self.critic_target = Critic(state_size, action_size, fc1_units,
fc2_units).to(device)
self.critic_optimizer = optim.Adam(self.critic_local.parameters(),
lr=LR_CRITIC,
weight_decay=WEIGHT_DECAY)
# Noise process
self.noise = OrnsteinUhlenbeck(action_size, SEED)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, SEED,
device)
def step(self, time_step, state, action, reward, next_state, done):
"""Save experience in replay memory, and use random sample from buffer to learn."""
self.memory.add(state, action, reward, next_state, done)
# Learn only every N_TIME_STEPS
if time_step % N_TIME_STEPS != 0:
return
# Learn if enough samples are available in replay buffer
if len(self.memory) > BATCH_SIZE:
for i in range(N_LEARN_UPDATES):
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 from 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()
torch.nn.utils.clip_grad_norm_(self.critic_local.parameters(), 1)
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)
def store(self):
torch.save(self.actor_local.state_dict(), 'checkpoint_actor.pth')
torch.save(self.critic_local.state_dict(), 'checkpoint_critic.pth')
def load(self):
if os.path.isfile('checkpoint_actor.pth') and os.path.isfile(
'checkpoint_critic.pth'):
print("=> loading checkpoints for Actor and Critic... ")
self.actor_local.load_state_dict('checkpoint_actor')
self.critic_local.load_state_dict('checkpoint_critic')
print("done !")
else:
print("no checkpoints found for Actor and Critic...")