class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size: int, action_size: int, seed: int,
n_agent: int):
"""Initialize an Agent object.
Params
======
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.n_agent = n_agent
self.seed = random.seed(seed)
self.global_step = 0
self.update_step = 0
# Initialize actor and critic local and target networks
self.actor = Actor(state_size,
action_size,
seed,
ACTOR_NETWORK_LINEAR_SIZES,
batch_normalization=ACTOR_BATCH_NORM).to(device)
self.actor_target = Actor(
state_size,
action_size,
seed,
ACTOR_NETWORK_LINEAR_SIZES,
batch_normalization=ACTOR_BATCH_NORM).to(device)
self.critic = Critic(state_size,
action_size,
seed,
CRITIC_NETWORK_LINEAR_SIZES,
batch_normalization=CRITIC_BATCH_NORM).to(device)
self.critic_second = Critic(
state_size,
action_size,
seed,
CRITIC_SECOND_NETWORK_LINEAR_SIZES,
batch_normalization=CRITIC_BATCH_NORM).to(device)
self.critic_second_target = Critic(
state_size,
action_size,
seed,
CRITIC_SECOND_NETWORK_LINEAR_SIZES,
batch_normalization=CRITIC_BATCH_NORM).to(device)
self.critic_target = Critic(
state_size,
action_size,
seed,
CRITIC_NETWORK_LINEAR_SIZES,
batch_normalization=CRITIC_BATCH_NORM).to(device)
self.actor_optimizer = optim.Adam(self.actor.parameters(),
lr=ACTOR_LEARNING_RATE)
self.critic_optimizer = optim.Adam(self.critic.parameters(),
lr=CRITIC_LEARNING_RATE)
self.critic_second_optimizer = optim.Adam(
self.critic_second.parameters(), lr=CRITIC_LEARNING_RATE)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
# Initialize time step (for updating every UPDATE_EVERY steps)
self.t_step = [0] * n_agent
self.noise = OUNoise(action_size, seed, decay_period=50)
# Copy parameters from local network to target network
for target_param, param in zip(self.actor_target.parameters(),
self.actor.parameters()):
target_param.data.copy_(param.data)
for target_param, param in zip(self.critic_target.parameters(),
self.critic.parameters()):
target_param.data.copy_(param.data)
for target_param, param in zip(self.critic_second_target.parameters(),
self.critic_second.parameters()):
target_param.data.copy_(param.data)
def step(self, state: np.array, action, reward, next_state, done, i_agent):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step[i_agent] = (self.t_step[i_agent] + 1) % UPDATE_EVERY
# Learn, if enough samples are available in memory every UPDATE_EVERY
if len(self.memory) > BATCH_SIZE and (not any(self.t_step)):
for _ in range(LEARN_TIMES):
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def noise_reset(self):
self.noise.reset()
def save_model(self, checkpoint_path: str = "./checkpoints/"):
torch.save(self.actor.state_dict(), f"{checkpoint_path}/actor.pt")
torch.save(self.critic.state_dict(), f"{checkpoint_path}/critic.pt")
def load_model(self, checkpoint_path: str = "./checkpoints/checkpoint.pt"):
self.actor.load_state_dict(torch.load(f"{checkpoint_path}/actor.pt"))
self.critic.load_state_dict(torch.load(f"{checkpoint_path}/critic.pt"))
def act(self, states: np.array, step: int):
"""Returns actions for given state as per current policy.
Params
======
state (array_like): current state
"""
self.global_step += 1
if self.global_step < WARM_UP_STEPS:
action_values = np.random.rand(self.n_agent, self.action_size)
return action_values
states = torch.from_numpy(states).float().to(device)
self.actor.eval()
with torch.no_grad():
action_values = self.actor(states).cpu().data.numpy()
self.actor.train()
action_values = [
self.noise.get_action(action, t=step) for action in action_values
]
return action_values
def learn(self, experiences: tuple, gamma=GAMMA):
"""Update value parameters using given batch of experience tuples.
Params
======
experiences (Tuple[torch.Variable]): tuple of (s, a, r, s', done) tuples
gamma (float): discount factor
"""
self.update_step += 1
states, actions, rewards, next_states, dones = experiences
# Critic loss
mask = torch.tensor(1 - dones).detach().to(device)
Q_values = self.critic(states, actions)
Q_values_second = self.critic_second(states, actions)
next_actions = self.actor_target(next_states)
next_Q = torch.min(
self.critic_target(next_states, next_actions.detach()),
self.critic_second_target(next_states, next_actions.detach()))
Q_prime = rewards + gamma * next_Q * mask
if self.update_step % CRITIC_UPDATE_EVERY == 0:
critic_loss = F.mse_loss(Q_values, Q_prime.detach())
critic_second_loss = F.mse_loss(Q_values_second, Q_prime.detach())
# Update first critic network
self.critic_optimizer.zero_grad()
critic_loss.backward()
if CRITIC_GRADIENT_CLIPPING_VALUE:
torch.nn.utils.clip_grad_norm_(self.critic.parameters(),
CRITIC_GRADIENT_CLIPPING_VALUE)
self.critic_optimizer.step()
# Update second critic network
self.critic_second_optimizer.zero_grad()
critic_second_loss.backward()
if CRITIC_GRADIENT_CLIPPING_VALUE:
torch.nn.utils.clip_grad_norm_(self.critic_second.parameters(),
CRITIC_GRADIENT_CLIPPING_VALUE)
self.critic_second_optimizer.step()
# Actor loss
if self.update_step % POLICY_UPDATE_EVERY == 0:
policy_loss = -self.critic(states, self.actor(states)).mean()
# Update actor network
self.actor_optimizer.zero_grad()
policy_loss.backward()
if ACTOR_GRADIENT_CLIPPING_VALUE:
torch.nn.utils.clip_grad_norm_(self.actor.parameters(),
ACTOR_GRADIENT_CLIPPING_VALUE)
self.actor_optimizer.step()
self.actor_soft_update()
self.critic_soft_update()
self.critic_second_soft_update()
def actor_soft_update(self, tau: float = TAU):
"""Soft update for actor target network
Args:
tau (float, optional). Defaults to TAU.
"""
for target_param, param in zip(self.actor_target.parameters(),
self.actor.parameters()):
target_param.data.copy_(tau * param.data +
(1.0 - tau) * target_param.data)
def critic_soft_update(self, tau: float = TAU):
"""Soft update for critic target network
Args:
tau (float, optional). Defaults to TAU.
"""
for target_param, param in zip(self.critic_target.parameters(),
self.critic.parameters()):
target_param.detach_()
target_param.data.copy_(tau * param.data +
(1.0 - tau) * target_param.data)
def critic_second_soft_update(self, tau: float = TAU):
"""Soft update for critic target network
Args:
tau (float, optional). Defaults to TAU.
"""
for target_param, param in zip(self.critic_second_target.parameters(),
self.critic_second.parameters()):
target_param.detach_()
target_param.data.copy_(tau * param.data +
(1.0 - tau) * target_param.data)
class BiCNet():
def __init__(self, s_dim, a_dim, n_agents, **kwargs):
self.s_dim = s_dim
self.a_dim = a_dim
self.config = kwargs['config']
self.n_agents = n_agents
self.device = 'cuda' if self.config.use_cuda else 'cpu'
# Networks
self.policy = Actor(s_dim, a_dim, n_agents)
self.policy_target = Actor(s_dim, a_dim, n_agents)
self.critic = Critic(s_dim, a_dim, n_agents)
self.critic_target = Critic(s_dim, a_dim, n_agents)
if self.config.use_cuda:
self.policy.cuda()
self.policy_target.cuda()
self.critic.cuda()
self.critic_target.cuda()
self.policy_optimizer = torch.optim.Adam(self.policy.parameters(),
lr=self.config.a_lr)
self.critic_optimizer = torch.optim.Adam(self.critic.parameters(),
lr=self.config.c_lr)
hard_update(self.policy, self.policy_target)
hard_update(self.critic, self.critic_target)
self.random_process = OrnsteinUhlenbeckProcess(
size=self.a_dim,
theta=self.config.ou_theta,
mu=self.config.ou_mu,
sigma=self.config.ou_sigma)
self.replay_buffer = list()
self.epsilon = 1.
self.depsilon = self.epsilon / self.config.epsilon_decay
self.c_loss = None
self.a_loss = None
self.action_log = list()
def choose_action(self, obs, noisy=True):
obs = torch.Tensor([obs]).to(self.device)
action = self.policy(obs).cpu().detach().numpy()[0]
self.action_log.append(action)
if noisy:
for agent_idx in range(self.n_agents):
pass
# action[agent_idx] += self.epsilon * self.random_process.sample()
self.epsilon -= self.depsilon
self.epsilon = max(self.epsilon, 0.001)
np.clip(action, -1., 1.)
return action
def reset(self):
self.random_process.reset_states()
self.action_log.clear()
def prep_train(self):
self.policy.train()
self.critic.train()
self.policy_target.train()
self.critic_target.train()
def prep_eval(self):
self.policy.eval()
self.critic.eval()
self.policy_target.eval()
self.critic_target.eval()
def random_action(self):
return np.random.uniform(low=-1, high=1, size=(self.n_agents, 2))
def memory(self, s, a, r, s_, done):
self.replay_buffer.append((s, a, r, s_, done))
if len(self.replay_buffer) >= self.config.memory_length:
self.replay_buffer.pop(0)
def get_batches(self):
experiences = random.sample(self.replay_buffer, self.config.batch_size)
state_batches = np.array([_[0] for _ in experiences])
action_batches = np.array([_[1] for _ in experiences])
reward_batches = np.array([_[2] for _ in experiences])
next_state_batches = np.array([_[3] for _ in experiences])
done_batches = np.array([_[4] for _ in experiences])
return state_batches, action_batches, reward_batches, next_state_batches, done_batches
def train(self):
state_batches, action_batches, reward_batches, next_state_batches, done_batches = self.get_batches(
)
state_batches = torch.Tensor(state_batches).to(self.device)
action_batches = torch.Tensor(action_batches).to(self.device)
reward_batches = torch.Tensor(reward_batches).reshape(
self.config.batch_size, self.n_agents, 1).to(self.device)
next_state_batches = torch.Tensor(next_state_batches).to(self.device)
done_batches = torch.Tensor(
(done_batches == False) * 1).reshape(self.config.batch_size,
self.n_agents,
1).to(self.device)
target_next_actions = self.policy_target.forward(next_state_batches)
target_next_q = self.critic_target.forward(next_state_batches,
target_next_actions)
main_q = self.critic(state_batches, action_batches)
'''
How to concat each agent's Q value?
'''
#target_next_q = target_next_q
#main_q = main_q.mean(dim=1)
'''
Reward Norm
'''
# reward_batches = (reward_batches - reward_batches.mean(dim=0)) / reward_batches.std(dim=0) / 1024
# Critic Loss
self.critic.zero_grad()
baselines = reward_batches + done_batches * self.config.gamma * target_next_q
loss_critic = torch.nn.MSELoss()(main_q, baselines.detach())
loss_critic.backward()
torch.nn.utils.clip_grad_norm_(self.critic.parameters(), 0.5)
self.critic_optimizer.step()
# Actor Loss
self.policy.zero_grad()
clear_action_batches = self.policy.forward(state_batches)
loss_actor = -self.critic.forward(state_batches,
clear_action_batches).mean()
loss_actor += (clear_action_batches**2).mean() * 1e-3
loss_actor.backward()
torch.nn.utils.clip_grad_norm_(self.policy.parameters(), 0.5)
self.policy_optimizer.step()
# This is for logging
self.c_loss = loss_critic.item()
self.a_loss = loss_actor.item()
soft_update(self.policy, self.policy_target, self.config.tau)
soft_update(self.critic, self.critic_target, self.config.tau)
def get_loss(self):
return self.c_loss, self.a_loss
def get_action_std(self):
return np.array(self.action_log).std(axis=-1).mean()
class DDPGAgent(object):
"""
General class for DDPG agents (policy, critic, target policy, target
critic, exploration noise)
"""
def __init__(self, num_in_pol, num_out_pol, num_in_critic, hidden_dim_actor=120,
hidden_dim_critic=64,lr_actor=0.01,lr_critic=0.01,batch_size=64,
max_episode_len=100,tau=0.02,gamma = 0.99,agent_name='one', discrete_action=False):
"""
Inputs:
num_in_pol (int): number of dimensions for policy input
num_out_pol (int): number of dimensions for policy output
num_in_critic (int): number of dimensions for critic input
"""
self.policy = Actor(num_in_pol, num_out_pol,
hidden_dim=hidden_dim_actor,
discrete_action=discrete_action)
self.critic = Critic(num_in_pol, 1,num_out_pol,
hidden_dim=hidden_dim_critic)
self.target_policy = Actor(num_in_pol, num_out_pol,
hidden_dim=hidden_dim_actor,
discrete_action=discrete_action)
self.target_critic = Critic(num_in_pol, 1,num_out_pol,
hidden_dim=hidden_dim_critic)
hard_update(self.target_policy, self.policy)
hard_update(self.target_critic, self.critic)
self.policy_optimizer = Adam(self.policy.parameters(), lr=lr_actor)
self.critic_optimizer = Adam(self.critic.parameters(), lr=lr_critic,weight_decay=0)
self.policy = self.policy.float()
self.critic = self.critic.float()
self.target_policy = self.target_policy.float()
self.target_critic = self.target_critic.float()
self.agent_name = agent_name
self.gamma = gamma
self.tau = tau
self.batch_size = batch_size
#self.replay_buffer = ReplayBuffer(1e7)
self.replay_buffer = ReplayBufferOption(500000,self.batch_size,12)
self.max_replay_buffer_len = batch_size * max_episode_len
self.replay_sample_index = None
self.niter = 0
self.eps = 5.0
self.eps_decay = 1/(250*5)
self.exploration = OUNoise(num_out_pol)
self.discrete_action = discrete_action
self.num_history = 2
self.states = []
self.actions = []
self.rewards = []
self.next_states = []
self.dones = []
def reset_noise(self):
if not self.discrete_action:
self.exploration.reset()
def scale_noise(self, scale):
if self.discrete_action:
self.exploration = scale
else:
self.exploration.scale = scale
def act(self, obs, explore=False):
"""
Take a step forward in environment for a minibatch of observations
Inputs:
obs : Observations for this agent
explore (boolean): Whether or not to add exploration noise
Outputs:
action (PyTorch Variable): Actions for this agent
"""
#obs = obs.reshape(1,48)
state = Variable(torch.Tensor(obs),requires_grad=False)
self.policy.eval()
with torch.no_grad():
action = self.policy(state)
self.policy.train()
# continuous action
if explore:
action += Variable(Tensor(self.eps * self.exploration.sample()),requires_grad=False)
action = torch.clamp(action, min=-1, max=1)
return action
def step(self, agent_id, state, action, reward, next_state, done,t_step):
self.states.append(state)
self.actions.append(action)
self.rewards.append(reward)
self.next_states.append(next_state)
self.dones.append(done)
#self.replay_buffer.add(state, action, reward, next_state, done)
if t_step % self.num_history == 0:
# Save experience / reward
self.replay_buffer.add(self.states, self.actions, self.rewards, self.next_states, self.dones)
self.states = []
self.actions = []
self.rewards = []
self.next_states = []
self.dones = []
# Learn, if enough samples are available in memory
if len(self.replay_buffer) > self.batch_size:
obs, acs, rews, next_obs, don = self.replay_buffer.sample()
self.update(agent_id ,obs, acs, rews, next_obs, don,t_step)
def update(self, agent_id, obs, acs, rews, next_obs, dones ,t_step, logger=None):
obs = torch.from_numpy(obs).float()
acs = torch.from_numpy(acs).float()
rews = torch.from_numpy(rews[:,agent_id]).float()
next_obs = torch.from_numpy(next_obs).float()
dones = torch.from_numpy(dones[:,agent_id]).float()
acs = acs.view(-1,2)
# --------- update critic ------------ #
self.critic_optimizer.zero_grad()
all_trgt_acs = self.target_policy(next_obs)
target_value = (rews + self.gamma *
self.target_critic(next_obs,all_trgt_acs) *
(1 - dones))
actual_value = self.critic(obs,acs)
vf_loss = MSELoss(actual_value, target_value.detach())
# Minimize the loss
vf_loss.backward()
#torch.nn.utils.clip_grad_norm_(self.critic.parameters(), 1)
self.critic_optimizer.step()
# --------- update actor --------------- #
self.policy_optimizer.zero_grad()
if self.discrete_action:
curr_pol_out = self.policy(obs)
curr_pol_vf_in = gumbel_softmax(curr_pol_out, hard=True)
else:
curr_pol_out = self.policy(obs)
curr_pol_vf_in = curr_pol_out
pol_loss = -self.critic(obs,curr_pol_vf_in).mean()
#pol_loss += (curr_pol_out**2).mean() * 1e-3
pol_loss.backward()
torch.nn.utils.clip_grad_norm_(self.policy.parameters(), 1)
self.policy_optimizer.step()
self.update_all_targets()
self.eps -= self.eps_decay
self.eps = max(self.eps, 0)
if logger is not None:
logger.add_scalars('agent%i/losses' % self.agent_name,
{'vf_loss': vf_loss,
'pol_loss': pol_loss},
self.niter)
def update_all_targets(self):
"""
Update all target networks (called after normal updates have been
performed for each agent)
"""
soft_update(self.critic, self.target_critic, self.tau)
soft_update(self.policy, self.target_policy, self.tau)
def get_params(self):
return {'policy': self.policy.state_dict(),
'critic': self.critic.state_dict(),
'target_policy': self.target_policy.state_dict(),
'target_critic': self.target_critic.state_dict(),
'policy_optimizer': self.policy_optimizer.state_dict(),
'critic_optimizer': self.critic_optimizer.state_dict()}
def load_params(self, params):
self.policy.load_state_dict(params['policy'])
self.critic.load_state_dict(params['critic'])
self.target_policy.load_state_dict(params['target_policy'])
self.target_critic.load_state_dict(params['target_critic'])
self.policy_optimizer.load_state_dict(params['policy_optimizer'])
self.critic_optimizer.load_state_dict(params['critic_optimizer'])
class DDPG:
def __init__(self, beta, epsilon, learning_rate, gamma, tau, hidden_size_dim0, hidden_size_dim1, num_inputs, action_space, train_mode, alpha, replay_size,
optimizer, two_player, normalize_obs=True, normalize_returns=False, critic_l2_reg=1e-2):
if torch.cuda.is_available():
self.device = torch.device('cuda')
torch.backends.cudnn.enabled = False
self.Tensor = torch.cuda.FloatTensor
else:
self.device = torch.device('cpu')
self.Tensor = torch.FloatTensor
self.alpha = alpha
self.train_mode = train_mode
self.num_inputs = num_inputs
self.action_space = action_space
self.critic_l2_reg = critic_l2_reg
self.actor = Actor(hidden_size_dim0, hidden_size_dim1, self.num_inputs, self.action_space).to(self.device)
self.adversary = Actor(hidden_size_dim0, hidden_size_dim1, self.num_inputs, self.action_space).to(self.device)
if self.train_mode:
self.actor_target = Actor(hidden_size_dim0, hidden_size_dim1, self.num_inputs, self.action_space).to(self.device)
self.actor_bar = Actor(hidden_size_dim0, hidden_size_dim1, self.num_inputs, self.action_space).to(self.device)
self.actor_outer = Actor(hidden_size_dim0, hidden_size_dim1, self.num_inputs, self.action_space).to(self.device)
if(optimizer == 'SGLD'):
self.actor_optim = SGLD(self.actor.parameters(), lr=1e-4, noise=epsilon, alpha=0.999)
elif(optimizer == 'RMSprop'):
self.actor_optim = RMSprop(self.actor.parameters(), lr=1e-4, alpha=0.999)
else:
self.actor_optim = ExtraAdam(self.actor.parameters(), lr=1e-4)
self.critic = Critic(hidden_size_dim0, hidden_size_dim1, self.num_inputs, self.action_space).to(self.device)
self.critic_target = Critic(hidden_size_dim0, hidden_size_dim1, self.num_inputs, self.action_space).to(self.device)
self.critic_optim = Adam(self.critic.parameters(), lr=1e-3, weight_decay=critic_l2_reg)
self.adversary_target = Actor(hidden_size_dim0, hidden_size_dim1, self.num_inputs, self.action_space).to(self.device)
self.adversary_bar = Actor(hidden_size_dim0, hidden_size_dim1, self.num_inputs, self.action_space).to(self.device)
self.adversary_outer = Actor(hidden_size_dim0, hidden_size_dim1, self.num_inputs, self.action_space).to(self.device)
if(optimizer == 'SGLD'):
self.adversary_optim = SGLD(self.adversary.parameters(), lr=1e-4, noise=epsilon, alpha=0.999)
elif(optimizer == 'RMSprop'):
self.adversary_optim = RMSprop(self.adversary.parameters(), lr=1e-4, alpha=0.999)
else:
self.adversary_optim = ExtraAdam(self.adversary.parameters(), lr=1e-4)
hard_update(self.adversary_target, self.adversary) # Make sure target is with the same weight
hard_update(self.actor_target, self.actor) # Make sure target is with the same weight
hard_update(self.critic_target, self.critic)
self.gamma = gamma
self.tau = tau
self.beta = beta
self.epsilon = epsilon
self.learning_rate = learning_rate
self.normalize_observations = normalize_obs
self.normalize_returns = normalize_returns
self.optimizer = optimizer
self.two_player = two_player
if self.normalize_observations:
self.obs_rms = RunningMeanStd(shape=num_inputs)
else:
self.obs_rms = None
if self.normalize_returns:
self.ret_rms = RunningMeanStd(shape=1)
self.ret = 0
self.cliprew = 10.0
else:
self.ret_rms = None
self.memory = ReplayMemory(replay_size)
def eval(self):
self.actor.eval()
self.adversary.eval()
if self.train_mode:
self.critic.eval()
def train(self):
self.actor.train()
self.adversary.train()
if self.train_mode:
self.critic.train()
def select_action(self, state, action_noise=None, param_noise=None, mdp_type='mdp'):
state = normalize(Variable(state).to(self.device), self.obs_rms, self.device)
if mdp_type != 'mdp':
if(self.optimizer == 'SGLD' and self.two_player):
mu = self.actor_outer(state)
else:
mu = self.actor(state)
mu = mu.data
if action_noise is not None:
mu += self.Tensor(action_noise()).to(self.device)
mu = mu.clamp(-1, 1) * (1 - self.alpha)
if(self.optimizer == 'SGLD' and self.two_player):
adv_mu = self.adversary_outer(state)
else:
adv_mu = self.adversary(state)
adv_mu = adv_mu.data.clamp(-1, 1) * self.alpha
mu += adv_mu
else:
if(self.optimizer == 'SGLD' and self.two_player):
mu = self.actor_outer(state)
else:
mu = self.actor(state)
mu = mu.data
if action_noise is not None:
mu += self.Tensor(action_noise()).to(self.device)
mu = mu.clamp(-1, 1)
return mu
def update_robust_non_flip(self, state_batch, action_batch, reward_batch, mask_batch, next_state_batch,
mdp_type, robust_update_type):
# TRAIN CRITIC
if robust_update_type == 'full':
next_action_batch = (1 - self.alpha) * self.actor_target(next_state_batch) \
+ self.alpha * self.adversary_target(next_state_batch)
next_state_action_values = self.critic_target(next_state_batch, next_action_batch)
expected_state_action_batch = reward_batch + self.gamma * mask_batch * next_state_action_values
self.critic_optim.zero_grad()
state_action_batch = self.critic(state_batch, action_batch)
value_loss = F.mse_loss(state_action_batch, expected_state_action_batch)
value_loss.backward()
self.critic_optim.step()
value_loss = value_loss.item()
else:
value_loss = 0
# TRAIN ADVERSARY
self.adversary_optim.zero_grad()
with torch.no_grad():
if(self.optimizer == 'SGLD' and self.two_player):
real_action = self.actor_outer(next_state_batch)
else:
real_action = self.actor_target(next_state_batch)
action = (1 - self.alpha) * real_action + self.alpha * self.adversary(next_state_batch)
adversary_loss = self.critic(state_batch, action)
adversary_loss = adversary_loss.mean()
adversary_loss.backward()
self.adversary_optim.step()
adversary_loss = adversary_loss.item()
# TRAIN ACTOR
self.actor_optim.zero_grad()
with torch.no_grad():
if(self.optimizer == 'SGLD' and self.two_player):
adversary_action = self.adversary_outer(next_state_batch)
else:
adversary_action = self.adversary_target(next_state_batch)
action = (1 - self.alpha) * self.actor(next_state_batch) + self.alpha * adversary_action
policy_loss = -self.critic(state_batch, action)
policy_loss = policy_loss.mean()
policy_loss.backward()
self.actor_optim.step()
policy_loss = policy_loss.item()
return value_loss, policy_loss, adversary_loss
def update_robust_flip(self, state_batch, action_batch, reward_batch, mask_batch, next_state_batch, adversary_update,
mdp_type, robust_update_type):
# TRAIN CRITIC
if robust_update_type == 'full':
next_action_batch = (1 - self.alpha) * self.actor_target(next_state_batch) \
+ self.alpha * self.adversary_target(next_state_batch)
next_state_action_values = self.critic_target(next_state_batch, next_action_batch)
expected_state_action_batch = reward_batch + self.gamma * mask_batch * next_state_action_values
self.critic_optim.zero_grad()
state_action_batch = self.critic(state_batch, action_batch)
value_loss = F.mse_loss(state_action_batch, expected_state_action_batch)
value_loss.backward()
self.critic_optim.step()
value_loss = value_loss.item()
else:
value_loss = 0
if adversary_update:
# TRAIN ADVERSARY
self.adversary_optim.zero_grad()
with torch.no_grad():
real_action = self.actor_target(next_state_batch)
action = (1 - self.alpha) * real_action + self.alpha * self.adversary(next_state_batch)
adversary_loss = self.critic(state_batch, action)
adversary_loss = adversary_loss.mean()
adversary_loss.backward()
self.adversary_optim.step()
adversary_loss = adversary_loss.item()
policy_loss = 0
else:
# TRAIN ACTOR
self.actor_optim.zero_grad()
with torch.no_grad():
adversary_action = self.adversary_target(next_state_batch)
action = (1 - self.alpha) * self.actor(next_state_batch) + self.alpha * adversary_action
policy_loss = -self.critic(state_batch, action)
policy_loss = policy_loss.mean()
policy_loss.backward()
self.actor_optim.step()
policy_loss = policy_loss.item()
adversary_loss = 0
return value_loss, policy_loss, adversary_loss
def update_non_robust(self, state_batch, action_batch, reward_batch, mask_batch, next_state_batch):
# TRAIN CRITIC
next_action_batch = self.actor_target(next_state_batch)
next_state_action_values = self.critic_target(next_state_batch, next_action_batch)
expected_state_action_batch = reward_batch + self.gamma * mask_batch * next_state_action_values
self.critic_optim.zero_grad()
state_action_batch = self.critic(state_batch, action_batch)
value_loss = F.mse_loss(state_action_batch, expected_state_action_batch)
value_loss.backward()
self.critic_optim.step()
# TRAIN ACTOR
self.actor_optim.zero_grad()
action = self.actor(next_state_batch)
policy_loss = -self.critic(state_batch, action)
policy_loss = policy_loss.mean()
policy_loss.backward()
self.actor_optim.step()
policy_loss = policy_loss.item()
adversary_loss = 0
return value_loss.item(), policy_loss, adversary_loss
def store_transition(self, state, action, mask, next_state, reward):
B = state.shape[0]
for b in range(B):
self.memory.push(state[b], action[b], mask[b], next_state[b], reward[b])
if self.normalize_observations:
self.obs_rms.update(state[b].cpu().numpy())
if self.normalize_returns:
self.ret = self.ret * self.gamma + reward[b]
self.ret_rms.update(np.array([self.ret]))
if mask[b] == 0: # if terminal is True
self.ret = 0
def update_parameters(self, batch_size, sgld_outer_update, mdp_type='mdp', exploration_method='mdp'):
transitions = self.memory.sample(batch_size)
batch = Transition(*zip(*transitions))
if mdp_type != 'mdp':
robust_update_type = 'full'
elif exploration_method != 'mdp':
robust_update_type = 'adversary'
else:
robust_update_type = None
state_batch = normalize(Variable(torch.stack(batch.state)).to(self.device), self.obs_rms, self.device)
action_batch = Variable(torch.stack(batch.action)).to(self.device)
reward_batch = normalize(Variable(torch.stack(batch.reward)).to(self.device).unsqueeze(1), self.ret_rms, self.device)
mask_batch = Variable(torch.stack(batch.mask)).to(self.device).unsqueeze(1)
next_state_batch = normalize(Variable(torch.stack(batch.next_state)).to(self.device), self.obs_rms, self.device)
if self.normalize_returns:
reward_batch = torch.clamp(reward_batch, -self.cliprew, self.cliprew)
value_loss = 0
policy_loss = 0
adversary_loss = 0
if robust_update_type is not None:
_value_loss, _policy_loss, _adversary_loss = self.update_robust_non_flip(state_batch, action_batch, reward_batch,
mask_batch, next_state_batch, mdp_type, robust_update_type)
value_loss += _value_loss
policy_loss += _policy_loss
adversary_loss += _adversary_loss
if robust_update_type != 'full':
_value_loss, _policy_loss, _adversary_loss = self.update_non_robust(state_batch, action_batch,
reward_batch,
mask_batch, next_state_batch)
value_loss += _value_loss
policy_loss += _policy_loss
adversary_loss += _adversary_loss
if(self.optimizer == 'SGLD' and self.two_player):
self.sgld_inner_update()
self.soft_update()
if(sgld_outer_update and self.optimizer == 'SGLD' and self.two_player):
self.sgld_outer_update()
return value_loss, policy_loss, adversary_loss
def initialize(self):
hard_update(self.actor_bar, self.actor_outer)
hard_update(self.adversary_bar, self.adversary_outer)
hard_update(self.actor, self.actor_outer)
hard_update(self.adversary, self.adversary_outer)
def sgld_inner_update(self): #target source
sgld_update(self.actor_bar, self.actor, self.beta)
sgld_update(self.adversary_bar, self.adversary, self.beta)
def sgld_outer_update(self): #target source
sgld_update(self.actor_outer, self.actor_bar, self.beta)
sgld_update(self.adversary_outer, self.adversary_bar, self.beta)
def soft_update(self):
soft_update(self.actor_target, self.actor, self.tau)
soft_update(self.adversary_target, self.adversary, self.tau)
soft_update(self.critic_target, self.critic, self.tau)
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, random_seed):
"""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 = 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)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE,
random_seed)
self.noise = OUNoise((action_size), random_seed)
# Make sure target is initialized with the same weight as the source (found on slack to make big difference)
self.hard_update(self.actor_target, self.actor_local)
self.hard_update(self.critic_target, self.critic_local)
def step(self, states, actions, rewards, next_states, dones):
"""Save experience in replay memory, and use random sample from buffer to learn."""
self.memory.add(states, actions, rewards, next_states, dones)
if len(self.memory) > BATCH_SIZE:
experiences = self.memory.sample()
self.learn(experiences, GAMMA)
def act(self, states, add_noise=True):
"""Returns actions for given state as per current policy."""
states = torch.from_numpy(states).float().to(device)
self.actor_local.eval()
with torch.no_grad():
actions = self.actor_local(states).cpu().data.numpy()
self.actor_local.train()
if add_noise:
actions += self.noise.sample()
return np.clip(actions, -1, 1)
def reset(self):
"""Noise reset."""
self.noise.reset()
self.i_step = 0
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, 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):
for target_param, source_param in zip(target.parameters(),
source.parameters()):
target_param.data.copy_(source_param.data)
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self,
state_size,
action_size,
aid=0,
num_agents=2,
seed=1234):
"""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
# Actor Network (w/ Target Network)
self.actor_local = Actor(state_size, action_size, seed=seed).to(device)
self.actor_target = Actor(state_size, action_size,
seed=seed).to(device)
self.actor_optimizer = Adam(self.actor_local.parameters(), lr=LR_ACTOR)
# Critic Network (w/ Target Network)
self.critic_local = Critic(state_size,
action_size,
num_agents=num_agents,
seed=seed).to(device)
self.critic_target = Critic(state_size,
action_size,
num_agents=num_agents,
seed=seed).to(device)
self.critic_optimizer = Adam(self.critic_local.parameters(),
lr=LR_CRITIC,
weight_decay=WEIGHT_DECAY)
# Noise process
self.noise = OUNoise(action_size, seed=seed)
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 update_targets(self, tau):
soft_update(self.critic_local, self.critic_target, tau)
soft_update(self.actor_local, self.actor_target, tau)
def update_critic(self, states, actions, next_states, next_actions,
rewards, dones):
Q_targets_next = self.critic_target(next_states, next_actions)
# 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()
def update_actor(self, states, actions):
actor_loss = -self.critic_local(states, actions).mean()
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
class DDPGAgent:
def __init__(self, state_size=24, action_size=2, seed=1, num_agents=2):
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(seed)
self.num_agents = num_agents
# DDPG specific configuration
hidden_size = 512
self.CHECKPOINT_FOLDER = './'
# Defining networks
self.actor = Actor(state_size, hidden_size, action_size).to(device)
self.actor_target = Actor(state_size, hidden_size, action_size).to(device)
self.critic = Critic(state_size, self.action_size, hidden_size, 1).to(device)
self.critic_target = Critic(state_size, self.action_size, hidden_size, 1).to(device)
self.optimizer_actor = optim.Adam(self.actor.parameters(), lr=ACTOR_LR)
self.optimizer_critic = optim.Adam(self.critic.parameters(), lr=CRITIC_LR)
# Noise
self.noises = OUNoise((num_agents, action_size), seed)
# Initialize replay buffer
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, seed)
def act(self, state, add_noise=True):
'''
Returns action to be taken based on state provided as the input
'''
state = torch.from_numpy(state).float().to(device)
actions = np.zeros((self.num_agents, self.action_size))
self.actor.eval()
with torch.no_grad():
for agent_num, state in enumerate(state):
action = self.actor(state).cpu().data.numpy()
actions[agent_num, :] = action
self.actor.train()
if add_noise:
actions += self.noises.sample()
return np.clip(actions, -1, 1)
def reset(self):
self.noises.reset()
def learn(self, experiences):
'''
Trains the actor critic network using experiences
'''
states, actions, rewards, next_states, dones = experiences
# Update Critic
actions_next = self.actor_target(next_states)
# print(next_states.shape, actions_next.shape)
Q_targets_next = self.critic_target(next_states, actions_next)
Q_targets = rewards + GAMMA*Q_targets_next*(1-dones)
Q_expected = self.critic(states, actions)
critic_loss = F.mse_loss(Q_expected, Q_targets)
self.optimizer_critic.zero_grad()
critic_loss.backward()
self.optimizer_critic.step()
# Update Actor
actions_pred = self.actor(states)
actor_loss = -self.critic(states, actions_pred).mean()
self.optimizer_actor.zero_grad()
actor_loss.backward()
self.optimizer_actor.step()
# Updating the local networks
self.soft_update(self.critic, self.critic_target)
self.soft_update(self.actor, self.actor_target)
def soft_update(self, model, model_target):
tau = TAU
for target_param, local_param in zip(model_target.parameters(), model.parameters()):
target_param.data.copy_(tau * local_param.data + (1.0 - tau) * target_param.data)
def step(self, state, action, reward, next_state, done):
'''
Adds experience to memory and learns if the memory contains sufficient samples
'''
for i in range(self.num_agents):
self.memory.add(state[i, :], action[i, :], reward[i], next_state[i, :], done[i])
if len(self.memory) > BATCH_SIZE:
# print("Now Learning")
experiences = self.memory.sample()
self.learn(experiences)
def checkpoint(self):
'''
Saves the actor critic network on disk
'''
torch.save(self.actor.state_dict(), self.CHECKPOINT_FOLDER + 'checkpoint_actor.pth')
torch.save(self.critic.state_dict(), self.CHECKPOINT_FOLDER + 'checkpoint_critic.pth')
def load(self):
'''
Loads the actor critic network from disk
'''
self.actor.load_state_dict(torch.load(self.CHECKPOINT_FOLDER + 'checkpoint_actor.pth'))
self.actor_target.load_state_dict(torch.load(self.CHECKPOINT_FOLDER + 'checkpoint_actor.pth'))
self.critic.load_state_dict(torch.load(self.CHECKPOINT_FOLDER + 'checkpoint_critic.pth'))
self.critic_target.load_state_dict(torch.load(self.CHECKPOINT_FOLDER + 'checkpoint_critic.pth'))
class Agent():
def __init__(self,
state_size,
action_size,
replay_memory,
random_seed=0,
nb_agent=20,
bs=128,
gamma=0.99,
tau=1e-3,
lr_actor=1e-4,
lr_critic=1e-4,
wd_actor=0,
wd_critic=0,
clip_actor=None,
clip_critic=None,
update_interval=20,
update_times=10):
self.state_size = state_size
self.action_size = action_size
self.seed = random.seed(random_seed)
self.nb_agent = nb_agent
self.bs = bs
self.update_interval = update_interval
self.update_times = update_times
self.timestep = 0
self.gamma = gamma
self.tau = tau
self.lr_actor = lr_actor
self.lr_critic = lr_critic
self.wd_critic = wd_critic
self.wd_actor = wd_actor
self.clip_critic = clip_critic
self.clip_actor = clip_actor
self.actor_losses = []
self.critic_losses = []
# 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,
weight_decay=self.wd_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.wd_critic)
# Noise process
self.noise = OUNoise((self.nb_agent, action_size), random_seed)
# Replay memory
self.memory = replay_memory
def step(self, states, actions, rewards, next_states, dones):
"""Save experience in replay memory, and use random sample from buffer to learn."""
#increment timestep
self.timestep += 1
# 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)
# Learn, if enough samples are available in memory
if self.timestep % self.update_interval == 0:
for i in range(self.update_times):
if len(self.memory) > self.bs:
experiences = self.memory.sample(self.bs)
self.learn(experiences)
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_noise(self):
self.noise.reset()
def learn(self, experiences):
"""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 + (self.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()
if self.clip_critic:
torch.nn.utils.clip_grad_norm(self.critic_local.parameters(),
self.clip_critic)
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()
if self.clip_actor:
torch.nn.utils.clip_grad_norm(self.actor_local.parameters(),
self.clip_actor)
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)
self.actor_losses.append(actor_loss.cpu().data.numpy())
self.critic_losses.append(critic_loss.cpu().data.numpy())
def soft_update(self, local_model, target_model):
"""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_(self.tau * local_param.data +
(1.0 - self.tau) * target_param.data)
class DDPG:
def __init__(self,
gamma,
tau,
hidden_size,
num_inputs,
action_space,
train_mode,
alpha,
replay_size,
normalize_obs=True,
normalize_returns=False,
critic_l2_reg=1e-2):
if torch.cuda.is_available():
self.device = torch.device('cuda')
torch.backends.cudnn.enabled = False
self.Tensor = torch.cuda.FloatTensor
else:
self.device = torch.device('cpu')
self.Tensor = torch.FloatTensor
self.alpha = alpha
self.train_mode = train_mode
self.num_inputs = num_inputs
self.action_space = action_space
self.critic_l2_reg = critic_l2_reg
self.actor = Actor(hidden_size, self.num_inputs,
self.action_space).to(self.device)
self.adversary = Actor(hidden_size, self.num_inputs,
self.action_space).to(self.device)
if self.train_mode:
self.actor_target = Actor(hidden_size, self.num_inputs,
self.action_space).to(self.device)
self.actor_perturbed = Actor(hidden_size, self.num_inputs,
self.action_space).to(self.device)
self.actor_optim = Adam(self.actor.parameters(), lr=1e-4)
self.critic = Critic(hidden_size, self.num_inputs,
self.action_space).to(self.device)
self.critic_target = Critic(hidden_size, self.num_inputs,
self.action_space).to(self.device)
self.critic_optim = Adam(self.critic.parameters(),
lr=1e-3,
weight_decay=critic_l2_reg)
self.adversary_target = Actor(hidden_size, self.num_inputs,
self.action_space).to(self.device)
self.adversary_perturbed = Actor(hidden_size, self.num_inputs,
self.action_space).to(self.device)
self.adversary_optim = Adam(self.adversary.parameters(), lr=1e-4)
hard_update(
self.adversary_target,
self.adversary) # Make sure target is with the same weight
hard_update(self.actor_target,
self.actor) # Make sure target is with the same weight
hard_update(self.critic_target, self.critic)
self.gamma = gamma
self.tau = tau
self.normalize_observations = normalize_obs
self.normalize_returns = normalize_returns
if self.normalize_observations:
self.obs_rms = RunningMeanStd(shape=num_inputs)
else:
self.obs_rms = None
if self.normalize_returns:
self.ret_rms = RunningMeanStd(shape=1)
self.ret = 0
self.cliprew = 10.0
else:
self.ret_rms = None
self.memory = ReplayMemory(replay_size)
def eval(self):
self.actor.eval()
self.adversary.eval()
if self.train_mode:
self.critic.eval()
def train(self):
self.actor.train()
self.adversary.train()
if self.train_mode:
self.critic.train()
def select_action(self,
state,
action_noise=None,
param_noise=None,
mdp_type='mdp'):
state = normalize(
Variable(state).to(self.device), self.obs_rms, self.device)
if mdp_type != 'mdp':
if mdp_type == 'nr_mdp':
if param_noise is not None:
mu = self.actor_perturbed(state)
else:
mu = self.actor(state)
mu = mu.data
if action_noise is not None:
mu += self.Tensor(action_noise()).to(self.device)
mu = mu.clamp(-1, 1) * (1 - self.alpha)
if param_noise is not None:
adv_mu = self.adversary_perturbed(state)
else:
adv_mu = self.adversary(state)
adv_mu = adv_mu.data.clamp(-1, 1) * self.alpha
mu += adv_mu
else: # mdp_type == 'pr_mdp':
if np.random.rand() < (1 - self.alpha):
if param_noise is not None:
mu = self.actor_perturbed(state)
else:
mu = self.actor(state)
mu = mu.data
if action_noise is not None:
mu += self.Tensor(action_noise()).to(self.device)
mu = mu.clamp(-1, 1)
else:
if param_noise is not None:
mu = self.adversary_perturbed(state)
else:
mu = self.adversary(state)
mu = mu.data.clamp(-1, 1)
else:
if param_noise is not None:
mu = self.actor_perturbed(state)
else:
mu = self.actor(state)
mu = mu.data
if action_noise is not None:
mu += self.Tensor(action_noise()).to(self.device)
mu = mu.clamp(-1, 1)
return mu
def update_robust(self, state_batch, action_batch, reward_batch,
mask_batch, next_state_batch, adversary_update, mdp_type,
robust_update_type):
# TRAIN CRITIC
if robust_update_type == 'full':
if mdp_type == 'nr_mdp':
next_action_batch = (1 - self.alpha) * self.actor_target(next_state_batch) \
+ self.alpha * self.adversary_target(next_state_batch)
next_state_action_values = self.critic_target(
next_state_batch, next_action_batch)
else: # mdp_type == 'pr_mdp':
next_action_actor_batch = self.actor_target(next_state_batch)
next_action_adversary_batch = self.adversary_target(
next_state_batch)
next_state_action_values = self.critic_target(next_state_batch, next_action_actor_batch) * (
1 - self.alpha) \
+ self.critic_target(next_state_batch,
next_action_adversary_batch) * self.alpha
expected_state_action_batch = reward_batch + self.gamma * mask_batch * next_state_action_values
self.critic_optim.zero_grad()
state_action_batch = self.critic(state_batch, action_batch)
value_loss = F.mse_loss(state_action_batch,
expected_state_action_batch)
value_loss.backward()
self.critic_optim.step()
value_loss = value_loss.item()
else:
value_loss = 0
if adversary_update:
# TRAIN ADVERSARY
self.adversary_optim.zero_grad()
if mdp_type == 'nr_mdp':
with torch.no_grad():
real_action = self.actor_target(next_state_batch)
action = (
1 - self.alpha
) * real_action + self.alpha * self.adversary(next_state_batch)
adversary_loss = self.critic(state_batch, action)
else: # mdp_type == 'pr_mdp'
action = self.adversary(next_state_batch)
adversary_loss = self.critic(state_batch, action) * self.alpha
adversary_loss = adversary_loss.mean()
adversary_loss.backward()
self.adversary_optim.step()
adversary_loss = adversary_loss.item()
policy_loss = 0
else:
if robust_update_type == 'full':
# TRAIN ACTOR
self.actor_optim.zero_grad()
if mdp_type == 'nr_mdp':
with torch.no_grad():
adversary_action = self.adversary_target(
next_state_batch)
action = (1 - self.alpha) * self.actor(
next_state_batch) + self.alpha * adversary_action
policy_loss = -self.critic(state_batch, action)
else: # mdp_type == 'pr_mdp':
action = self.actor(next_state_batch)
policy_loss = -self.critic(state_batch, action) * (
1 - self.alpha)
policy_loss = policy_loss.mean()
policy_loss.backward()
self.actor_optim.step()
policy_loss = policy_loss.item()
adversary_loss = 0
else:
policy_loss = 0
adversary_loss = 0
return value_loss, policy_loss, adversary_loss
def update_non_robust(self, state_batch, action_batch, reward_batch,
mask_batch, next_state_batch):
# TRAIN CRITIC
next_action_batch = self.actor_target(next_state_batch)
next_state_action_values = self.critic_target(next_state_batch,
next_action_batch)
expected_state_action_batch = reward_batch + self.gamma * mask_batch * next_state_action_values
self.critic_optim.zero_grad()
state_action_batch = self.critic(state_batch, action_batch)
value_loss = F.mse_loss(state_action_batch,
expected_state_action_batch)
value_loss.backward()
self.critic_optim.step()
# TRAIN ACTOR
self.actor_optim.zero_grad()
action = self.actor(next_state_batch)
policy_loss = -self.critic(state_batch, action)
policy_loss = policy_loss.mean()
policy_loss.backward()
self.actor_optim.step()
policy_loss = policy_loss.item()
adversary_loss = 0
return value_loss.item(), policy_loss, adversary_loss
def store_transition(self, state, action, mask, next_state, reward):
B = state.shape[0]
for b in range(B):
self.memory.push(state[b], action[b], mask[b], next_state[b],
reward[b])
if self.normalize_observations:
self.obs_rms.update(state[b].cpu().numpy())
if self.normalize_returns:
self.ret = self.ret * self.gamma + reward[b]
self.ret_rms.update(np.array([self.ret]))
if mask[b] == 0: # if terminal is True
self.ret = 0
def update_parameters(self,
batch_size,
mdp_type='mdp',
adversary_update=False,
exploration_method='mdp'):
transitions = self.memory.sample(batch_size)
batch = Transition(*zip(*transitions))
if mdp_type != 'mdp':
robust_update_type = 'full'
elif exploration_method != 'mdp':
robust_update_type = 'adversary'
else:
robust_update_type = None
state_batch = normalize(
Variable(torch.stack(batch.state)).to(self.device), self.obs_rms,
self.device)
action_batch = Variable(torch.stack(batch.action)).to(self.device)
reward_batch = normalize(
Variable(torch.stack(batch.reward)).to(self.device).unsqueeze(1),
self.ret_rms, self.device)
mask_batch = Variable(torch.stack(batch.mask)).to(
self.device).unsqueeze(1)
next_state_batch = normalize(
Variable(torch.stack(batch.next_state)).to(self.device),
self.obs_rms, self.device)
if self.normalize_returns:
reward_batch = torch.clamp(reward_batch, -self.cliprew,
self.cliprew)
value_loss = 0
policy_loss = 0
adversary_loss = 0
if robust_update_type is not None:
_value_loss, _policy_loss, _adversary_loss = self.update_robust(
state_batch, action_batch, reward_batch, mask_batch,
next_state_batch, adversary_update, mdp_type,
robust_update_type)
value_loss += _value_loss
policy_loss += _policy_loss
adversary_loss += _adversary_loss
if robust_update_type != 'full':
_value_loss, _policy_loss, _adversary_loss = self.update_non_robust(
state_batch, action_batch, reward_batch, mask_batch,
next_state_batch)
value_loss += _value_loss
policy_loss += _policy_loss
adversary_loss += _adversary_loss
self.soft_update()
return value_loss, policy_loss, adversary_loss
def soft_update(self):
soft_update(self.actor_target, self.actor, self.tau)
soft_update(self.adversary_target, self.adversary, self.tau)
soft_update(self.critic_target, self.critic, self.tau)
def perturb_actor_parameters(self, param_noise):
"""Apply parameter noise to actor model, for exploration"""
hard_update(self.actor_perturbed, self.actor)
params = self.actor_perturbed.state_dict()
for name in params:
if 'ln' in name:
pass
param = params[name]
param += torch.randn(param.shape).to(
self.device) * param_noise.current_stddev
"""Apply parameter noise to adversary model, for exploration"""
hard_update(self.adversary_perturbed, self.adversary)
params = self.adversary_perturbed.state_dict()
for name in params:
if 'ln' in name:
pass
param = params[name]
param += torch.randn(param.shape).to(
self.device) * param_noise.current_stddev
class DDPG(Policy):
def __init__(self,
gamma,
tau,
num_inputs,
action_space,
replay_size,
normalize_obs=True,
normalize_returns=False,
critic_l2_reg=1e-2,
num_outputs=1,
entropy_coeff=0.1,
action_coeff=0.1):
super(DDPG, self).__init__(gamma=gamma,
tau=tau,
num_inputs=num_inputs,
action_space=action_space,
replay_size=replay_size,
normalize_obs=normalize_obs,
normalize_returns=normalize_returns)
self.num_outputs = num_outputs
self.entropy_coeff = entropy_coeff
self.action_coeff = action_coeff
self.critic_l2_reg = critic_l2_reg
self.actor = Actor(self.num_inputs, self.action_space,
self.num_outputs).to(self.device)
self.actor_target = Actor(self.num_inputs, self.action_space,
self.num_outputs).to(self.device)
self.actor_perturbed = Actor(self.num_inputs, self.action_space,
self.num_outputs).to(self.device)
self.actor_optim = Adam(self.actor.parameters(), lr=1e-4)
self.critic = Critic(self.num_inputs + self.action_space.shape[0]).to(
self.device)
self.critic_target = Critic(self.num_inputs +
self.action_space.shape[0]).to(self.device)
self.critic_optim = Adam(self.critic.parameters(),
lr=1e-3,
weight_decay=critic_l2_reg)
hard_update(self.actor_target,
self.actor) # Make sure target is with the same weight
hard_update(self.critic_target, self.critic)
def eval(self):
self.actor.eval()
self.critic.eval()
def train(self):
self.actor.train()
self.critic.train()
def policy(self, actor, state):
return actor(state), None
def update_critic(self, state_batch, action_batch, reward_batch,
mask_batch, next_state_batch):
batch_size = state_batch.shape[0]
with torch.no_grad():
tiled_next_state_batch = self._tile(next_state_batch, 0,
self.num_outputs)
next_action_batch, _, next_probs, _ = self.actor_target(
next_state_batch)
next_state_action_values = (self.critic_target(
tiled_next_state_batch,
next_action_batch.view(
batch_size * self.num_outputs, -1))[0].view(
batch_size, self.num_outputs) *
next_probs).sum(-1).unsqueeze(-1)
expected_state_action_batch = reward_batch + self.gamma * mask_batch * next_state_action_values
self.critic_optim.zero_grad()
state_action_batch = self.critic(state_batch, action_batch)[0]
value_loss = F.mse_loss(state_action_batch,
expected_state_action_batch)
value_loss.backward()
self.critic_optim.step()
return value_loss.item()
def update_actor(self, state_batch):
batch_size = state_batch.shape[0]
tiled_state_batch = self._tile(state_batch, 0, self.num_outputs)
action_batch, _, probs, dist_entropy = self.actor(state_batch)
policy_loss = -(self.critic_target(
tiled_state_batch,
action_batch.view(batch_size * self.num_outputs, -1))[0].view(
batch_size, self.num_outputs) * probs).sum(-1)
entropy_loss = dist_entropy * self.entropy_coeff
action_mse = 0
action_batch = action_batch.view(batch_size, self.num_outputs, -1)
for idx1 in range(self.num_outputs):
for idx2 in range(idx1 + 1, self.num_outputs):
action_mse += (
(action_batch[:, idx1, :] - action_batch[:, idx2, :])**
2).mean() * self.action_coeff / self.num_outputs
return policy_loss - entropy_loss - action_mse
def soft_update(self):
soft_update(self.actor_target, self.actor, self.tau)
soft_update(self.critic_target, self.critic, self.tau)
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, random_seed):
"""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 = random.seed(random_seed)
self.t_step = 0
# 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)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE, random_seed)
def step(self, state, action, reward, next_state, done):
# Save experience in replay memory
self.memory.add(state, action, reward, next_state, done)
# Learn every UPDATE_EVERY time steps.
self.t_step = (self.t_step + 1)
if self.t_step % UPDATE_EVERY == 0:
# If enough samples are available in memory, get random subset and learn
if len(self.memory) > BATCH_SIZE:
for i 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 += 0.4 * 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()
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)
class Agent:
def __init__(self, replay_buffer, noise, state_dim, action_dim, seed, fc1_units = 256, fc2_units = 128,
device="cpu", lr_actor=1e-4, lr_critic=1e-3, batch_size=128, discount=0.99, tau=1e-3):
torch.manual_seed(seed)
self.actor_local = Actor(state_dim, action_dim, fc1_units, fc2_units, seed).to(device)
self.critic_local = Critic(state_dim, action_dim, fc1_units, fc2_units, seed).to(device)
self.actor_optimizer = optim.Adam(params=self.actor_local.parameters(), lr=lr_actor)
self.critic_optimizer = optim.Adam(params=self.critic_local.parameters(), lr=lr_critic)
self.actor_target = Actor(state_dim, action_dim, fc1_units, fc2_units, seed).to(device)
self.critic_target = Critic(state_dim, action_dim, fc1_units, fc2_units, seed).to(device)
self.buffer = replay_buffer
self.noise = noise
self.device = device
self.batch_size = batch_size
self.discount = discount
self.tau = tau
Agent.hard_update(model_local=self.actor_local, model_target=self.actor_target)
Agent.hard_update(model_local=self.critic_local, model_target=self.critic_target)
def step(self, states, actions, rewards, next_states, dones):
for state, action, reward, next_state, done in zip(states, actions, rewards, next_states, dones):
self.buffer.add(state=state, action=action, reward=reward, next_state=next_state, done=done)
if self.buffer.size() >= self.batch_size:
experiences = self.buffer.sample(self.batch_size)
self.learn(self.to_tensor(experiences))
def to_tensor(self, experiences):
states, actions, rewards, next_states, dones = experiences
states = torch.from_numpy(states).float().to(self.device)
actions = torch.from_numpy(actions).float().to(self.device)
rewards = torch.from_numpy(rewards).float().to(self.device)
next_states = torch.from_numpy(next_states).float().to(self.device)
dones = torch.from_numpy(dones.astype(np.uint8)).float().to(self.device)
return states, actions, rewards, next_states, dones
def act(self, states, add_noise=True):
states = torch.from_numpy(states).float().to(device=self.device)
self.actor_local.eval()
with torch.no_grad():
actions = self.actor_local(states).data.numpy()
self.actor_local.train()
if add_noise:
actions += self.noise.sample()
return np.clip(actions, -1, 1)
def learn(self, experiences):
states, actions, rewards, next_states, dones = experiences
# Update critic
next_actions = self.actor_target(next_states)
q_target_next = self.critic_target(next_states, next_actions)
q_target = rewards + self.discount * q_target_next * (1.0 - dones)
q_local = self.critic_local(states, actions)
critic_loss = F.mse_loss(input=q_local, target=q_target)
self.critic_local.zero_grad()
critic_loss.backward()
self.critic_optimizer.step()
actor_objective = self.critic_local(states, self.actor_local(states)).mean()
self.actor_local.zero_grad()
(-actor_objective).backward()
self.actor_optimizer.step()
Agent.soft_update(model_local=self.critic_local, model_target=self.critic_target, tau=self.tau)
Agent.soft_update(model_local=self.actor_local, model_target=self.actor_target, tau=self.tau)
@staticmethod
def soft_update(model_local, model_target, tau):
for local_param, target_param in zip(model_local.parameters(), model_target.parameters()):
target_param.data.copy_(tau * local_param.data + (1.0 - tau) * target_param.data)
@staticmethod
def hard_update(model_local, model_target):
Agent.soft_update(model_local=model_local, model_target=model_target, tau=1.0)
def reset(self):
self.noise.reset()
class Agent():
"""Interacts with and learns from the environment."""
def __init__(self, state_size, action_size, num_agents, random_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
random_seed (int): random seed
"""
self.state_size = state_size
self.action_size = action_size
self.num_agents = num_agents
self.seed = random.seed(random_seed)
self.eps = eps_start
self.eps_decay = 1 / (eps_p * LEARN_NUM
) # set decay rate based on epsilon end target
# 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((num_agents, action_size), random_seed)
# Replay memory
self.memory = ReplayBuffer(action_size, BUFFER_SIZE, BATCH_SIZE,
random_seed)
def step(self, state, action, reward, next_state, done, agent_number,
timestep):
"""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 at learning interval settings
if len(self.memory) > BATCH_SIZE and timestep % 1 == 0:
for _ in range(LEARN_NUM):
experiences = self.memory.sample()
self.learn(experiences, GAMMA, agent_number)
def act(self, states, add_noise):
"""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()
# add noise to actions
if add_noise:
actions += self.eps * self.noise.sample()
actions = np.clip(actions, -1, 1)
return actions
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[:, :2], actions_next), dim=1)
else:
actions_next = torch.cat((actions_next, actions[:, 2:]), dim=1)
# Compute Q targets for current states (y_i)
Q_targets_next = self.critic_target(next_states, actions_next)
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)
# Construct action prediction vector relative to each agent
if agent_number != 0:
actions_pred = torch.cat((actions[:, :2], actions_pred), dim=1)
else:
actions_pred = torch.cat((actions_pred, actions[:, 2:]), dim=1)
# Compute actor loss
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 noise decay parameter
self.eps -= self.eps_decay
self.eps = max(self.eps, eps_end)
self.noise.reset()
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)
