def __init__(self, id, state_size, action_size, seed, memory, num_agents,
hyperparameters: Mapping[str, float]):
"""Initialize a DDPG agent object.
Params
======
id (int): agent's id
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
memory (ReplayBuffer): replay buffer to store the experience of this agent
hyperparameters (dictionnary of str:): hyperparameters' values of the model. The expected parameters are:
- batch_size (int): minibatch size
- lr_actor (float): learning rate of the actor
- lr_critic (float): learning rate of the critic
- gamma (float): discount factor
- weight_decay (float): critic L2 weight decay
- tau (float): value for soft update of target parameters
- update_frequency (int): how much steps must be executed before starting learn
- n_learns (int): how many learning for update
"""
self.id = id
self.__name__ = 'DDPG'
self.state_size = state_size
self.action_size = action_size
self.gamma = hyperparameters['gamma']
self.batch_size = int(hyperparameters['batch_size'])
self.tau = hyperparameters['tau']
self.update_frequency = int(hyperparameters['update_frequency'])
self.n_learns = int(hyperparameters['n_learns'])
# Actor Network (w/ Target Network)
self.actor_local = Actor(state_size, action_size, seed).to(device)
self.actor_target = Actor(state_size, action_size, seed).to(device)
self.actor_optimizer = optim.Adam(self.actor_local.parameters(),
lr=hyperparameters['lr_actor'])
# Critic Network (w/ Target Network)
self.critic_local = Critic(state_size, action_size, num_agents,
seed).to(device)
self.critic_target = Critic(state_size, action_size, num_agents,
seed).to(device)
self.critic_optimizer = optim.Adam(
self.critic_local.parameters(),
lr=hyperparameters['lr_critic'],
weight_decay=hyperparameters['weight_decay'])
# Noise process
self.noise = Ornstein(action_size)
# Replay memory
self.memory = memory
# Initialize the time step (for every update_frequency steps)
self.t_step = 0
def __init__(self,
env,
gamma,
tau,
buffer_maxlen,
critic_learning_rate,
actor_learning_rate,
max_action=1):
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.env = env
self.obs_dim = env.observation_space.shape[0]
self.action_dim = env.action_space.shape[0]
self.noise = OUNoise(env.action_space)
self.iter = 0.0
self.noisy = False
self.max_action = max_action
print(self.action_dim)
print(self.obs_dim)
# RL hyperparameters
self.env = env
self.gamma = gamma
self.tau = tau
# Initialize critic and actorr networks
self.critic = Critic(self.obs_dim, self.action_dim).to(self.device)
self.critic_target = Critic(self.obs_dim,
self.action_dim).to(self.device)
self.actor = Actor(self.obs_dim, self.action_dim,
self.max_action).to(self.device)
self.actor_target = Actor(self.obs_dim,
self.action_dim).to(self.device)
# Copy target network paramters for critic
for target_param, param in zip(self.critic_target.parameters(),
self.critic.parameters()):
target_param.data.copy_(param.data)
# Set Optimization algorithms
self.critic_optimizer = optim.Adam(self.critic.parameters(),
lr=critic_learning_rate)
self.actor_optimizer = optim.Adam(self.actor.parameters(),
lr=actor_learning_rate)
self.replay_buffer = ExperienceReplayLog(buffer_maxlen)
def train(env_id, *args, **kwargs):
"""Train a DDPG model on the given environment."""
env = gym.make(env_id)
[obs_dim] = env.observation_space.shape
[act_dim] = env.action_space.shape
# tf global variables are created here so they will be initialized by
# @run_with_sess when we call train_with
critic = Critic(obs_dim, act_dim)
actor = Actor(obs_dim, act_dim, critic)
noise = ornstein_uhlenbeck_noise(np.zeros(act_dim))
return train_with(env, actor, critic, noise, *args, **kwargs)
def __init__(self, logger, obs_dim, action_space, userconfig):
super().__init__(logger=logger,
obs_dim=obs_dim,
action_dim=action_space.shape[0],
userconfig=userconfig)
self._observation_dim = obs_dim
self._action_space = action_space
self._action_n = action_space.shape[0]
self._config = {
"eps": 0.05,
"discount": 0.95,
"buffer_size": int(1e5),
"batch_size": 128,
"learning_rate_actor": 0.0002,
"learning_rate_critic": 0.0002,
"hidden_sizes": [256, 256],
'tau': 0.0001
}
self._config.update(userconfig)
self._eps = self._config['eps']
self._tau = self._config['tau']
self.device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
self.eval_mode = False
if self._config['lr_milestones'] is None:
raise ValueError(
'lr_milestones argument cannot be None!\nExample: --lr_milestones=100 200 300'
)
lr_milestones = [
int(x) for x in (self._config['lr_milestones'][0]).split(' ')
]
# Critic
self.critic = Critic(
self._observation_dim,
self._action_n,
hidden_sizes=self._config['hidden_sizes'],
learning_rate=self._config['learning_rate_critic'],
lr_milestones=lr_milestones,
lr_factor=self._config['lr_factor'],
device=self._config['device'])
self.critic_target = Critic(
self._observation_dim,
self._action_n,
hidden_sizes=self._config['hidden_sizes'],
learning_rate=self._config['learning_rate_critic'],
lr_milestones=lr_milestones,
lr_factor=self._config['lr_factor'],
device=self._config['device'])
# Actor
self.actor = Actor(self._observation_dim,
self._action_n,
hidden_sizes=self._config['hidden_sizes'],
learning_rate=self._config['learning_rate_actor'],
lr_milestones=lr_milestones,
lr_factor=self._config['lr_factor'],
device=self._config['device'])
self.actor_target = Actor(
self._observation_dim,
self._action_n,
hidden_sizes=self._config['hidden_sizes'],
learning_rate=self._config['learning_rate_actor'],
lr_milestones=lr_milestones,
lr_factor=self._config['lr_factor'],
device=self._config['device'])
class DDPGAgent(Agent):
"""
The DDPGAgent class implements a trainable DDPG agent.
Parameters
----------
logger: Logger
The variable specifies a logger for model management, plotting and printing.
obs_dim: int
The variable specifies the dimension of observation space vector.
action_space: ndarray
The variable specifies the action space of environment.
userconfig:
The variable specifies the config settings.
"""
def __init__(self, logger, obs_dim, action_space, userconfig):
super().__init__(logger=logger,
obs_dim=obs_dim,
action_dim=action_space.shape[0],
userconfig=userconfig)
self._observation_dim = obs_dim
self._action_space = action_space
self._action_n = action_space.shape[0]
self._config = {
"eps": 0.05,
"discount": 0.95,
"buffer_size": int(1e5),
"batch_size": 128,
"learning_rate_actor": 0.0002,
"learning_rate_critic": 0.0002,
"hidden_sizes": [256, 256],
'tau': 0.0001
}
self._config.update(userconfig)
self._eps = self._config['eps']
self._tau = self._config['tau']
self.device = torch.device(
'cuda' if torch.cuda.is_available() else 'cpu')
self.eval_mode = False
if self._config['lr_milestones'] is None:
raise ValueError(
'lr_milestones argument cannot be None!\nExample: --lr_milestones=100 200 300'
)
lr_milestones = [
int(x) for x in (self._config['lr_milestones'][0]).split(' ')
]
# Critic
self.critic = Critic(
self._observation_dim,
self._action_n,
hidden_sizes=self._config['hidden_sizes'],
learning_rate=self._config['learning_rate_critic'],
lr_milestones=lr_milestones,
lr_factor=self._config['lr_factor'],
device=self._config['device'])
self.critic_target = Critic(
self._observation_dim,
self._action_n,
hidden_sizes=self._config['hidden_sizes'],
learning_rate=self._config['learning_rate_critic'],
lr_milestones=lr_milestones,
lr_factor=self._config['lr_factor'],
device=self._config['device'])
# Actor
self.actor = Actor(self._observation_dim,
self._action_n,
hidden_sizes=self._config['hidden_sizes'],
learning_rate=self._config['learning_rate_actor'],
lr_milestones=lr_milestones,
lr_factor=self._config['lr_factor'],
device=self._config['device'])
self.actor_target = Actor(
self._observation_dim,
self._action_n,
hidden_sizes=self._config['hidden_sizes'],
learning_rate=self._config['learning_rate_actor'],
lr_milestones=lr_milestones,
lr_factor=self._config['lr_factor'],
device=self._config['device'])
def eval(self):
self.eval_mode = True
def train_mode(self):
self.eval_mode = False
def act(self, observation, eps=0, evaluation=False):
state = torch.from_numpy(observation).float().to(self.device)
if eps is None:
eps = self._eps
if np.random.random() > eps or evaluation:
action = self.actor.forward(state)
action = action.detach().cpu().numpy()[0]
else:
action = self._action_space.sample()[:4]
return action
def schedulers_step(self):
self.critic.lr_scheduler.step()
self.critic_target.lr_scheduler.step()
self.actor.lr_scheduler.step()
self.actor_target.lr_scheduler.step()
def store_transition(self, transition):
self.buffer.add_transition(transition)
@staticmethod
def load_model(fpath):
with open(Path(fpath), 'rb') as inp:
return pickle.load(inp)
def train(self, total_step_counter, iter_fit=32):
losses = []
for i in range(iter_fit):
data = self.buffer.sample(batch_size=self._config['batch_size'])
s = torch.FloatTensor(np.stack(data[:, 0])).to(self.device)
s_next = torch.FloatTensor(np.stack(data[:, 3])).to(self.device)
a = torch.FloatTensor(np.stack(
data[:, 1])[:, None]).squeeze(dim=1).to(self.device)
rew = torch.FloatTensor(np.stack(
data[:, 2])[:, None]).squeeze(dim=1).to(self.device)
done = torch.FloatTensor(np.stack(
data[:, 4])[:,
None]).squeeze(dim=1).to(self.device) # done flag
Q_target = self.critic(s, a).squeeze(dim=1).to(self.device)
a_next = self.actor_target.forward(s_next)
Q_next = self.critic_target.forward(
s_next, a_next).squeeze(dim=1).to(self.device)
# target
targets = rew + self._config['discount'] * Q_next * (1.0 - done)
# optimize critic
targets = targets.to(self.device)
critic_loss = self.critic.loss(Q_target.float(), targets.float())
losses.append(critic_loss)
self.critic.optimizer.zero_grad()
critic_loss.backward()
self.critic.optimizer.step()
actions = self.actor.forward(s)
actor_loss = -self.critic.forward(s, actions).mean()
self.actor.optimizer.zero_grad()
actor_loss.backward()
self.actor.optimizer.step()
# update
if (total_step_counter) % self._config['update_target_every'] == 0:
# optimize actor
soft_update(self.critic_target, self.critic, self._tau)
soft_update(self.actor_target, self.actor, self._tau)
return losses
def retraining(
save_path,
network,
env,
seed=None,
total_timesteps=None,
nb_epochs=None, # with default settings, perform 1M steps total
nb_epoch_cycles=4, #50
nb_rollout_steps=3, #100
reward_scale=1.0,
render=False,
render_eval=False,
# noise_type='adaptive-param_0.2',
noise_type='normal_0.2',
# noise_type='ou_0.9',
normalize_returns=False,
normalize_observations=True,
critic_l2_reg=1e-2,
actor_lr=1e-4,
critic_lr=1e-4,
# actor_lr=1e-6,
# critic_lr=1e-5,
popart=False,
gamma=0.99,
clip_norm=None,
nb_train_steps=3, # per epoch cycle and MPI worker, 50
nb_eval_steps=1, #100
batch_size=640, # per MPI worker
tau=0.01,
eval_env=None,
param_noise_adaption_interval=3, #50
**network_kwargs):
if total_timesteps is not None:
assert nb_epochs is None
nb_epochs = int(total_timesteps) // (nb_epoch_cycles *
nb_rollout_steps)
else:
nb_epochs = 500
rank = MPI.COMM_WORLD.Get_rank()
# nb_actions = env.action_space.shape[-1]
nb_actions = env.num_actions
# nb_actions=3
# print(nb_actions)
action_shape = np.array(nb_actions * [0]).shape
#4 pairs pos + 3 link length
# nb_features = 2*(env.num_actions+1)+env.num_actions
#4 pairs pos + 1 pair target pos
nb_features = 2 * (env.num_actions + 2)
observation_shape = np.array(nb_features * [0]).shape
# assert (np.abs(env.action_space.low) == env.action_space.high).all() # we assume symmetric actions.
# memory = Memory(limit=int(1e6), action_shape=env.action_space.shape, observation_shape=env.observation_space.shape)
memory = Memory(limit=int(1e6),
action_shape=action_shape,
observation_shape=observation_shape)
critic = Critic(network=network, **network_kwargs)
actor = Actor(nb_actions, network=network, **network_kwargs)
action_noise = None
param_noise = None
# nb_actions = env.action_space.shape[-1]
if noise_type is not None:
for current_noise_type in noise_type.split(','):
current_noise_type = current_noise_type.strip()
if current_noise_type == 'none':
pass
elif 'adaptive-param' in current_noise_type:
_, stddev = current_noise_type.split('_')
param_noise = AdaptiveParamNoiseSpec(
initial_stddev=float(stddev),
desired_action_stddev=float(stddev))
elif 'normal' in current_noise_type:
_, stddev = current_noise_type.split('_')
action_noise = NormalActionNoise(mu=np.zeros(nb_actions),
sigma=float(stddev) *
np.ones(nb_actions))
elif 'ou' in current_noise_type:
_, stddev = current_noise_type.split('_')
action_noise = OrnsteinUhlenbeckActionNoise(
mu=np.zeros(nb_actions),
sigma=float(stddev) * np.ones(nb_actions))
else:
raise RuntimeError(
'unknown noise type "{}"'.format(current_noise_type))
# agent = DDPG(actor, critic, memory, env.observation_space.shape, env.action_space.shape,
agent = DDPG(actor,
critic,
memory,
observation_shape,
action_shape,
gamma=gamma,
tau=tau,
normalize_returns=normalize_returns,
normalize_observations=normalize_observations,
batch_size=batch_size,
action_noise=action_noise,
param_noise=param_noise,
critic_l2_reg=critic_l2_reg,
actor_lr=actor_lr,
critic_lr=critic_lr,
enable_popart=popart,
clip_norm=clip_norm,
reward_scale=reward_scale)
logger.info('Using agent with the following configuration:')
logger.info(str(agent.__dict__.items()))
eval_episode_rewards_history = deque(maxlen=100)
episode_rewards_history = deque(maxlen=100)
sess = U.get_session()
# Prepare everything.
agent.initialize(sess)
# sess.graph.finalize()
agent.reset()
obs = env.reset()
if eval_env is not None:
eval_obs = eval_env.reset()
nenvs = obs.shape[0]
episode_reward = np.zeros(nenvs, dtype=np.float32) #vector
episode_step = np.zeros(nenvs, dtype=int) # vector
episodes = 0 #scalar
t = 0 # scalar
step_set = []
reward_set = []
epoch = 0
start_time = time.time()
epoch_episode_rewards = []
mean_epoch_episode_rewards = []
epoch_episode_steps = []
epoch_actions = []
epoch_qs = []
epoch_episodes = 0
#load the initialization policy
agent.load_ini(sess, save_path)
# agent.memory.clear(limit=int(1e6), action_shape=action_shape, observation_shape=observation_shape)
for epoch in range(nb_epochs):
print(nb_epochs)
# obs, env_state = env.reset()
obs = env.reset()
agent.save(save_path)
epoch_episode_rewards = []
'''check if the actor initialization policy has been loaded correctly,
i.e. equal to directly ouput values in checkpoint files '''
# loaded_weights=tf.get_default_graph().get_tensor_by_name('target_actor/mlp_fc0/w:0')
# print('loaded_weights:', sess.run(loaded_weights))
for cycle in range(nb_epoch_cycles):
# Perform rollouts.
for t_rollout in range(nb_rollout_steps):
# Predict next action
action, q, _, _ = agent.step(obs,
apply_noise=True,
compute_Q=True)
print('action:', action)
new_obs, r, done = env.step(action)
# time.sleep(0.2)
t += 1
episode_reward += r
episode_step += 1
# print('episode_re: ', episode_reward) #[1.]
# Book-keeping.
epoch_actions.append(action)
epoch_qs.append(q)
b = 1.
agent.store_transition(
obs, action, r, new_obs, done
) #the batched data will be unrolled in memory.py's append.
obs = new_obs
epoch_episode_rewards.append(episode_reward)
episode_reward = np.zeros(nenvs, dtype=np.float32) #vector
# Train.
epoch_actor_losses = []
epoch_critic_losses = []
epoch_adaptive_distances = []
for t_train in range(nb_train_steps):
# Adapt param noise, if necessary.
if memory.nb_entries >= batch_size and t_train % param_noise_adaption_interval == 0:
distance = agent.adapt_param_noise()
epoch_adaptive_distances.append(distance)
# print('Train!')
cl, al = agent.train()
epoch_critic_losses.append(cl)
epoch_actor_losses.append(al)
agent.update_target_net()
# Evaluate.
eval_episode_rewards = []
eval_qs = []
if eval_env is not None:
nenvs_eval = eval_obs.shape[0]
eval_episode_reward = np.zeros(nenvs_eval, dtype=np.float32)
for t_rollout in range(nb_eval_steps):
eval_action, eval_q, _, _ = agent.step(eval_obs,
apply_noise=False,
compute_Q=True)
# eval_obs, eval_r, eval_done, eval_info = eval_env.step(max_action * eval_action) # scale for execution in env (as far as DDPG is concerned, every action is in [-1, 1])
eval_obs, eval_r, eval_done, eval_info = eval_env.step(
eval_action)
if render_eval:
eval_env.render()
eval_episode_reward += eval_r
eval_qs.append(eval_q)
for d in range(len(eval_done)):
if eval_done[d]:
eval_episode_rewards.append(eval_episode_reward[d])
eval_episode_rewards_history.append(
eval_episode_reward[d])
eval_episode_reward[d] = 0.0
mpi_size = MPI.COMM_WORLD.Get_size()
# Log stats.
# XXX shouldn't call np.mean on variable length lists
duration = time.time() - start_time
stats = agent.get_stats()
combined_stats = stats.copy()
combined_stats['rollout/return'] = np.mean(epoch_episode_rewards)
combined_stats['rollout/return_history'] = np.mean(
episode_rewards_history)
combined_stats['rollout/episode_steps'] = np.mean(epoch_episode_steps)
combined_stats['rollout/actions_mean'] = np.mean(epoch_actions)
combined_stats['rollout/Q_mean'] = np.mean(epoch_qs)
combined_stats['train/loss_actor'] = np.mean(epoch_actor_losses)
combined_stats['train/loss_critic'] = np.mean(epoch_critic_losses)
combined_stats['train/param_noise_distance'] = np.mean(
epoch_adaptive_distances)
combined_stats['total/duration'] = duration
combined_stats['total/steps_per_second'] = float(t) / float(duration)
combined_stats['total/episodes'] = episodes
combined_stats['rollout/episodes'] = epoch_episodes
combined_stats['rollout/actions_std'] = np.std(epoch_actions)
mean_epoch_episode_rewards.append(np.mean(epoch_episode_rewards))
# print(step_set,mean_epoch_episode_rewards)
step_set.append(t)
plt.plot(step_set,
mean_epoch_episode_rewards,
color='r',
label='Initialization')
plt.xlabel('Steps')
plt.ylabel('Mean Episode Reward')
plt.savefig('ddpg_mean_retrain.png')
# plt.show()
# Evaluation statistics.
if eval_env is not None:
combined_stats['eval/return'] = eval_episode_rewards
combined_stats['eval/return_history'] = np.mean(
eval_episode_rewards_history)
combined_stats['eval/Q'] = eval_qs
combined_stats['eval/episodes'] = len(eval_episode_rewards)
def as_scalar(x):
if isinstance(x, np.ndarray):
assert x.size == 1
return x[0]
elif np.isscalar(x):
return x
else:
raise ValueError('expected scalar, got %s' % x)
combined_stats_sums = MPI.COMM_WORLD.allreduce(
np.array(
[np.array(x).flatten()[0] for x in combined_stats.values()]))
combined_stats = {
k: v / mpi_size
for (k, v) in zip(combined_stats.keys(), combined_stats_sums)
}
# Total statistics.
combined_stats['total/epochs'] = epoch + 1
combined_stats['total/steps'] = t
for key in sorted(combined_stats.keys()):
logger.record_tabular(key, combined_stats[key])
if rank == 0:
logger.dump_tabular()
logger.info('')
logdir = logger.get_dir()
if rank == 0 and logdir:
if hasattr(env, 'get_state'):
with open(os.path.join(logdir, 'env_state.pkl'), 'wb') as f:
pickle.dump(env.get_state(), f)
if eval_env and hasattr(eval_env, 'get_state'):
with open(os.path.join(logdir, 'eval_env_state.pkl'),
'wb') as f:
pickle.dump(eval_env.get_state(), f)
print('stepset: ', step_set)
print('rewards: ', mean_epoch_episode_rewards)
return agent
class DDPGAgent(object):
"""Interacts with and learns from the environment."""
def __init__(self, id, state_size, action_size, seed, memory, num_agents,
hyperparameters: Mapping[str, float]):
"""Initialize a DDPG agent object.
Params
======
id (int): agent's id
state_size (int): dimension of each state
action_size (int): dimension of each action
seed (int): random seed
memory (ReplayBuffer): replay buffer to store the experience of this agent
hyperparameters (dictionnary of str:): hyperparameters' values of the model. The expected parameters are:
- batch_size (int): minibatch size
- lr_actor (float): learning rate of the actor
- lr_critic (float): learning rate of the critic
- gamma (float): discount factor
- weight_decay (float): critic L2 weight decay
- tau (float): value for soft update of target parameters
- update_frequency (int): how much steps must be executed before starting learn
- n_learns (int): how many learning for update
"""
self.id = id
self.__name__ = 'DDPG'
self.state_size = state_size
self.action_size = action_size
self.gamma = hyperparameters['gamma']
self.batch_size = int(hyperparameters['batch_size'])
self.tau = hyperparameters['tau']
self.update_frequency = int(hyperparameters['update_frequency'])
self.n_learns = int(hyperparameters['n_learns'])
# Actor Network (w/ Target Network)
self.actor_local = Actor(state_size, action_size, seed).to(device)
self.actor_target = Actor(state_size, action_size, seed).to(device)
self.actor_optimizer = optim.Adam(self.actor_local.parameters(),
lr=hyperparameters['lr_actor'])
# Critic Network (w/ Target Network)
self.critic_local = Critic(state_size, action_size, num_agents,
seed).to(device)
self.critic_target = Critic(state_size, action_size, num_agents,
seed).to(device)
self.critic_optimizer = optim.Adam(
self.critic_local.parameters(),
lr=hyperparameters['lr_critic'],
weight_decay=hyperparameters['weight_decay'])
# Noise process
self.noise = Ornstein(action_size)
# Replay memory
self.memory = memory
# Initialize the time step (for every update_frequency steps)
self.t_step = 0
def step(self, state, action, reward, next_state, done, other_states,
other_actions, other_next_states):
"""Save experience in replay memory, and use random sample from buffer to learn."""
self.memory.add(state, action, reward, next_state, done, other_states,
other_actions, other_next_states)
self.t_step = (self.t_step + 1) % self.update_frequency
if self.t_step == 0:
# Learn, if enough samples are available in memory
for _ in range(self.n_learns):
if len(self.memory) > self.batch_size:
experiences = self.memory.sample(self.batch_size)
self.learn(experiences, self.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():
action = self.actor_local(states).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, _, _, _, _, other_states, _, _ = experiences
self.update_critic(experiences, gamma)
self.update_actor(states, other_states)
self.update_target_networks()
def update_critic(self, experiences, gamma):
"""Update the critic network given the experiences"""
states, actions, rewards, next_states, dones, other_states, other_actions, other_next_states = experiences
all_states = torch.cat((states, other_states), dim=1).to(device)
all_actions = torch.cat((actions, other_actions), dim=1).to(device)
all_next_states = torch.cat((next_states, other_next_states),
dim=1).to(device)
local_all_next_actions = []
local_all_next_actions.append(self.actor_target(states))
local_all_next_actions.append(self.actor_target(other_states))
all_next_actions = torch.cat(local_all_next_actions, dim=1).to(device)
# ---------------------------- update critic ---------------------------- #
# Get predicted next-state actions and Q values from target models
Q_targets_next = self.critic_target(all_next_states, all_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(all_states, all_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()
def update_actor(self, states, other_states):
all_states = torch.cat((states, other_states), dim=1).to(device)
# ---------------------------- update actor ---------------------------- #
# Compute actor loss
actions_pred = self.actor_local(states)
other_actions_pred = self.actor_local(other_states)
other_actions_pred = other_actions_pred.detach()
actions_pred = torch.cat((actions_pred, other_actions_pred),
dim=1).to(device)
actor_loss = -self.critic_local(all_states, actions_pred).mean()
# Minimize the loss
self.actor_optimizer.zero_grad()
actor_loss.backward()
self.actor_optimizer.step()
def update_target_networks(self):
# ----------------------- update target networks ----------------------- #
self.soft_update(self.critic_local, self.critic_target, self.tau)
self.soft_update(self.actor_local, self.actor_target, self.tau)
def soft_update(self, local_model, target_model, tau):
"""Soft update model parameters.
θ_target = τ*θ_local + (1 - τ)*θ_target
Params
======
local_model: PyTorch model (weights will be copied from)
target_model: PyTorch model (weights will be copied to)
tau (float): interpolation parameter
"""
for target_param, local_param in zip(target_model.parameters(),
local_model.parameters()):
target_param.data.copy_(tau * local_param.data +
(1.0 - tau) * target_param.data)
def testing(
save_path,
network,
env,
seed=None,
total_timesteps=None,
nb_epochs=None, # with default settings, perform 1M steps total
nb_epoch_cycles=50,
nb_rollout_steps=3,
reward_scale=1.0,
render=False,
render_eval=False,
# no noise for test
# noise_type='adaptive-param_0.2',
# noise_type='normal_0.9',
# noise_type='ou_0.9',
normalize_returns=False,
normalize_observations=True,
critic_l2_reg=1e-2,
actor_lr=1e-4,
critic_lr=1e-3,
# actor_lr=1e-6,
# critic_lr=1e-5,
popart=False,
gamma=0.99,
clip_norm=None,
nb_train_steps=3, # per epoch cycle and MPI worker, 50
nb_eval_steps=1,
batch_size=64, # per MPI worker
tau=0.01,
eval_env=None,
param_noise_adaption_interval=3, #
**network_kwargs):
if total_timesteps is not None:
assert nb_epochs is None
nb_epochs = int(total_timesteps) // (nb_epoch_cycles *
nb_rollout_steps)
else:
nb_epochs = 500
rank = MPI.COMM_WORLD.Get_rank()
# nb_actions = env.action_space.shape[-1]
# nb_actions = 2*env.grid_size
nb_actions = env.grid_size
action_shape = np.array(nb_actions * [0]).shape
nb_features = (4 + 1) * env.grid_size
observation_shape = np.array(nb_features * [0]).shape
grid_x = env.grid_x
grid_y = env.grid_y
x = []
y = []
for i in range(grid_x):
x.append(i + 1)
for i in range(grid_y):
y.append(i + 1)
# assert (np.abs(env.action_space.low) == env.action_space.high).all() # we assume symmetric actions.
# memory = Memory(limit=int(1e6), action_shape=env.action_space.shape, observation_shape=env.observation_space.shape)
memory = Memory(limit=int(1e6),
action_shape=action_shape,
observation_shape=observation_shape)
critic = Critic(network=network, **network_kwargs)
actor = Actor(nb_actions, network=network, **network_kwargs)
action_noise = None
param_noise = None
'''no noise for test'''
# if noise_type is not None:
# for current_noise_type in noise_type.split(','):
# current_noise_type = current_noise_type.strip()
# if current_noise_type == 'none':
# pass
# elif 'adaptive-param' in current_noise_type:
# _, stddev = current_noise_type.split('_')
# param_noise = AdaptiveParamNoiseSpec(initial_stddev=float(stddev), desired_action_stddev=float(stddev))
# elif 'normal' in current_noise_type:
# _, stddev = current_noise_type.split('_')
# action_noise = NormalActionNoise(mu=np.zeros(nb_actions), sigma=float(stddev) * np.ones(nb_actions))
# elif 'ou' in current_noise_type:
# _, stddev = current_noise_type.split('_')
# action_noise = OrnsteinUhlenbeckActionNoise(mu=np.zeros(nb_actions), sigma=float(stddev) * np.ones(nb_actions))
# else:
# raise RuntimeError('unknown noise type "{}"'.format(current_noise_type))
# max_action = env.action_space.high
# logger.info('scaling actions by {} before executing in env'.format(max_action))
# agent = DDPG(actor, critic, memory, env.observation_space.shape, env.action_space.shape,
agent = DDPG(actor,
critic,
memory,
observation_shape,
action_shape,
gamma=gamma,
tau=tau,
normalize_returns=normalize_returns,
normalize_observations=normalize_observations,
batch_size=batch_size,
action_noise=action_noise,
param_noise=param_noise,
critic_l2_reg=critic_l2_reg,
actor_lr=actor_lr,
critic_lr=critic_lr,
enable_popart=popart,
clip_norm=clip_norm,
reward_scale=reward_scale)
logger.info('Using agent with the following configuration:')
logger.info(str(agent.__dict__.items()))
eval_episode_rewards_history = deque(maxlen=100)
episode_rewards_history = deque(maxlen=100)
sess = U.get_session()
# Prepare everything.
# agent.initialize(sess)
# sess.graph.finalize()
agent.load(sess, save_path)
agent.reset()
obs, env_state = env.reset()
if eval_env is not None:
eval_obs = eval_env.reset()
nenvs = obs.shape[0]
episode_reward = np.zeros(nenvs, dtype=np.float32) #vector
episode_step = np.zeros(nenvs, dtype=int) # vector
episodes = 0 #scalar
t = 0 # scalar
step_set = []
reward_set = []
epoch = 0
start_time = time.time()
epoch_episode_rewards = []
average_reward = []
mean_epoch_episode_rewards = []
epoch_episode_steps = []
epoch_actions = []
epoch_qs = []
epoch_state = []
epoch_episodes = 0
#record the car numbers in each step
car_num_set = {}
t_set = [i for i in range(total_timesteps)]
for xx in x:
for yy in y:
lab = str(xx) + str(yy)
car_num_set[lab] = [[0 for i in range(total_timesteps)]
for j in range(4)]
for epoch in range(nb_epochs):
obs, env_state = env.reset()
epoch_actions = []
epoch_state = []
average_car_num_set = []
last_action = 1
for cycle in range(nb_epoch_cycles):
# Perform rollouts.
action, q, _, _ = agent.step(obs,
apply_noise=False,
compute_Q=True)
'''random action'''
# if np.random.rand()>0.5:
# action=[1]
# else:
# action=[0]
'''cycle light state'''
# action=[0]
'''cycle action (should cycle state instead of action)'''
# if last_action==1:
# action=[0]
# else:
# action=[1]
# last_action=action[0]
if nenvs > 1:
# if simulating multiple envs in parallel, impossible to reset agent at the end of the episode in each
# of the environments, so resetting here instead
agent.reset()
for t_rollout in range(nb_rollout_steps):
new_obs, r, env_state, done = env.step(action, env_state)
epoch_state.append(env_state['11'].light_state)
for xx in x:
for yy in y:
lab = str(xx) + str(yy)
for i in range(4):
car_num_set[lab][i][t] = (
env_state['11'].car_nums[i])
t += 1
episode_reward += r
episode_step += 1
# Book-keeping.
epoch_actions.append(action)
epoch_qs.append(q)
b = 1.
agent.store_transition(
obs, action, r, new_obs, done
) #the batched data will be unrolled in memory.py's append.
obs = new_obs
for d in range(len(done)):
if done[d]:
print('done')
# Episode done.
epoch_episode_rewards.append(episode_reward[d])
episode_rewards_history.append(episode_reward[d])
epoch_episode_steps.append(episode_step[d])
episode_reward[d] = 0.
episode_step[d] = 0
epoch_episodes += 1
episodes += 1
if nenvs == 1:
agent.reset()
epoch_episode_rewards.append(episode_reward)
average_reward.append(episode_reward / nb_rollout_steps)
episode_reward = np.zeros(nenvs, dtype=np.float32) #vector
# Train.
epoch_actor_losses = []
epoch_critic_losses = []
epoch_adaptive_distances = []
# for t_train in range(nb_train_steps):
# # Adapt param noise, if necessary.
# if memory.nb_entries >= batch_size and t_train % param_noise_adaption_interval == 0:
# distance = agent.adapt_param_noise()
# epoch_adaptive_distances.append(distance)
# # print('Train!')
# cl, al = agent.train()
# epoch_critic_losses.append(cl)
# epoch_actor_losses.append(al)
# agent.update_target_net()
# Evaluate.
eval_episode_rewards = []
eval_qs = []
if eval_env is not None:
nenvs_eval = eval_obs.shape[0]
eval_episode_reward = np.zeros(nenvs_eval, dtype=np.float32)
for t_rollout in range(nb_eval_steps):
eval_action, eval_q, _, _ = agent.step(eval_obs,
apply_noise=False,
compute_Q=True)
# eval_obs, eval_r, eval_done, eval_info = eval_env.step(max_action * eval_action) # scale for execution in env (as far as DDPG is concerned, every action is in [-1, 1])
eval_obs, eval_r, eval_done, eval_info = eval_env.step(
eval_action)
if render_eval:
eval_env.render()
eval_episode_reward += eval_r
eval_qs.append(eval_q)
for d in range(len(eval_done)):
if eval_done[d]:
eval_episode_rewards.append(eval_episode_reward[d])
eval_episode_rewards_history.append(
eval_episode_reward[d])
eval_episode_reward[d] = 0.0
step_set.append(t)
mpi_size = MPI.COMM_WORLD.Get_size()
# Log stats.
# XXX shouldn't call np.mean on variable length lists
duration = time.time() - start_time
stats = agent.get_stats()
combined_stats = stats.copy()
combined_stats['rollout/return'] = np.mean(epoch_episode_rewards)
combined_stats['rollout/return_history'] = np.mean(
episode_rewards_history)
combined_stats['rollout/episode_steps'] = np.mean(epoch_episode_steps)
combined_stats['rollout/actions_mean'] = np.mean(epoch_actions)
combined_stats['rollout/Q_mean'] = np.mean(epoch_qs)
combined_stats['train/loss_actor'] = np.mean(epoch_actor_losses)
combined_stats['train/loss_critic'] = np.mean(epoch_critic_losses)
combined_stats['train/param_noise_distance'] = np.mean(
epoch_adaptive_distances)
combined_stats['total/duration'] = duration
combined_stats['total/steps_per_second'] = float(t) / float(duration)
combined_stats['total/episodes'] = episodes
combined_stats['rollout/episodes'] = epoch_episodes
combined_stats['rollout/actions_std'] = np.std(epoch_actions)
mean_epoch_episode_rewards.append(np.mean(epoch_episode_rewards))
# print(step_set,mean_epoch_episode_rewards)
# plt.figure(figsize=(8,5))
'''plot rewards-steps'''
ax1 = plt.subplot(2, 1, 1)
plt.sca(ax1)
plt.plot(step_set, average_reward, color='b')
# plt.xlabel('Steps')
plt.ylabel('Mean Reward', fontsize=12)
# plt.ylim(-15000,0)
'''plot queueing car numbers-steps'''
ax2 = plt.subplot(2, 1, 2)
plt.sca(ax2)
print(np.shape(t_set), np.shape(car_num_set['11'][i]))
for i in range(4):
if i == 0:
plt.plot(t_set, car_num_set['11'][i], '--', label=i, color='b')
elif i == 1:
plt.plot(t_set,
car_num_set['11'][i],
'--',
label=i,
color='orange')
elif i == 2:
plt.plot(t_set, car_num_set['11'][i], label=i, color='g')
else:
plt.plot(t_set, car_num_set['11'][i], label=i, color='r')
plt.ylim(0, 100)
#sum among roads
sum_car_num = np.sum(car_num_set['11'], axis=0)
#average among time steps
average_car_num = np.average(sum_car_num)
average_car_num_set.append(average_car_num)
plt.xlabel('Steps', fontsize=12)
plt.ylabel('Cars Numbers', fontsize=12)
# set legend
handles, labels = plt.gca().get_legend_handles_labels()
by_label = OrderedDict(zip(labels, handles))
leg = plt.legend(by_label.values(), by_label.keys(), loc=1)
# leg = plt.legend(loc=4)
legfm = leg.get_frame()
legfm.set_edgecolor('black') # set legend fame color
legfm.set_linewidth(0.5) # set legend fame linewidth
plt.savefig('ddpg_mean_test.pdf')
plt.show()
print(epoch_state)
# Evaluation statistics.
if eval_env is not None:
combined_stats['eval/return'] = eval_episode_rewards
combined_stats['eval/return_history'] = np.mean(
eval_episode_rewards_history)
combined_stats['eval/Q'] = eval_qs
combined_stats['eval/episodes'] = len(eval_episode_rewards)
def as_scalar(x):
if isinstance(x, np.ndarray):
assert x.size == 1
return x[0]
elif np.isscalar(x):
return x
else:
raise ValueError('expected scalar, got %s' % x)
combined_stats_sums = MPI.COMM_WORLD.allreduce(
np.array(
[np.array(x).flatten()[0] for x in combined_stats.values()]))
combined_stats = {
k: v / mpi_size
for (k, v) in zip(combined_stats.keys(), combined_stats_sums)
}
# Total statistics.
combined_stats['total/epochs'] = epoch + 1
combined_stats['total/steps'] = t
for key in sorted(combined_stats.keys()):
logger.record_tabular(key, combined_stats[key])
if rank == 0:
logger.dump_tabular()
logger.info('')
logdir = logger.get_dir()
if rank == 0 and logdir:
if hasattr(env, 'get_state'):
with open(os.path.join(logdir, 'env_state.pkl'), 'wb') as f:
pickle.dump(env.get_state(), f)
if eval_env and hasattr(eval_env, 'get_state'):
with open(os.path.join(logdir, 'eval_env_state.pkl'),
'wb') as f:
pickle.dump(eval_env.get_state(), f)
print('average queueing car numbers: ', np.average(average_car_num_set))
return agent
def testing(save_path, network, env,
seed=None,
total_timesteps=None,
nb_epochs=None, # with default settings, perform 1M steps total
nb_epoch_cycles=50,
nb_rollout_steps=3, #100
reward_scale=1.0,
render=False,
render_eval=False,
# no noise for test
# noise_type='adaptive-param_0.2',
# noise_type='normal_0.9',
# noise_type='ou_0.9',
normalize_returns=False,
normalize_observations=True,
critic_l2_reg=1e-2,
actor_lr=1e-4,
critic_lr=1e-3,
# actor_lr=1e-6,
# critic_lr=1e-5,
popart=False,
gamma=0.99,
clip_norm=None,
nb_train_steps=3, # per epoch cycle and MPI worker, 50
nb_eval_steps=1, #100
batch_size=640, # per MPI worker
tau=0.01,
eval_env=None,
param_noise_adaption_interval=3, #50
**network_kwargs):
if total_timesteps is not None:
assert nb_epochs is None
nb_epochs = int(total_timesteps) // (nb_epoch_cycles * nb_rollout_steps)
else:
nb_epochs = 500
rank = MPI.COMM_WORLD.Get_rank()
# nb_actions = env.action_space.shape[-1]
nb_actions = env.num_actions
# nb_actions=3
# print(nb_actions)
action_shape=np.array(nb_actions*[0]).shape
nb_features = 2*(env.num_actions+1)+env.num_actions
observation_shape=np.array(nb_features*[0]).shape
# assert (np.abs(env.action_space.low) == env.action_space.high).all() # we assume symmetric actions.
# memory = Memory(limit=int(1e6), action_shape=env.action_space.shape, observation_shape=env.observation_space.shape)
memory = Memory(limit=int(1e6), action_shape=action_shape, observation_shape=observation_shape)
critic = Critic(network=network, **network_kwargs)
actor = Actor(nb_actions, network=network, **network_kwargs)
action_noise = None
param_noise = None
# nb_actions = env.action_space.shape[-1]
'''no noise for test'''
# if noise_type is not None:
# for current_noise_type in noise_type.split(','):
# current_noise_type = current_noise_type.strip()
# if current_noise_type == 'none':
# pass
# elif 'adaptive-param' in current_noise_type:
# _, stddev = current_noise_type.split('_')
# param_noise = AdaptiveParamNoiseSpec(initial_stddev=float(stddev), desired_action_stddev=float(stddev))
# elif 'normal' in current_noise_type:
# _, stddev = current_noise_type.split('_')
# action_noise = NormalActionNoise(mu=np.zeros(nb_actions), sigma=float(stddev) * np.ones(nb_actions))
# elif 'ou' in current_noise_type:
# _, stddev = current_noise_type.split('_')
# action_noise = OrnsteinUhlenbeckActionNoise(mu=np.zeros(nb_actions), sigma=float(stddev) * np.ones(nb_actions))
# else:
# raise RuntimeError('unknown noise type "{}"'.format(current_noise_type))
# max_action = env.action_space.high
# logger.info('scaling actions by {} before executing in env'.format(max_action))
# agent = DDPG(actor, critic, memory, env.observation_space.shape, env.action_space.shape,
agent = DDPG(actor, critic, memory, observation_shape, action_shape,
gamma=gamma, tau=tau, normalize_returns=normalize_returns, normalize_observations=normalize_observations,
batch_size=batch_size, action_noise=action_noise, param_noise=param_noise, critic_l2_reg=critic_l2_reg,
actor_lr=actor_lr, critic_lr=critic_lr, enable_popart=popart, clip_norm=clip_norm,
reward_scale=reward_scale)
logger.info('Using agent with the following configuration:')
logger.info(str(agent.__dict__.items()))
eval_episode_rewards_history = deque(maxlen=100)
episode_rewards_history = deque(maxlen=100)
sess = U.get_session()
# Prepare everything.
agent.load(sess,save_path)
# sess.graph.finalize() # cannot save sess if its finalized!
agent.reset()
obs = env.reset()
if eval_env is not None:
eval_obs = eval_env.reset()
nenvs = obs.shape[0]
episode_reward = np.zeros(nenvs, dtype = np.float32) #vector
episode_step = np.zeros(nenvs, dtype = int) # vector
episodes = 0 #scalar
t = 0 # scalar
step_set=[]
reward_set=[]
epoch = 0
start_time = time.time()
epoch_episode_rewards = []
mean_epoch_episode_rewards = []
epoch_episode_steps = []
epoch_actions = []
epoch_qs = []
epoch_episodes = 0
for epoch in range(nb_epochs):
print(nb_epochs)
# obs, env_state = env.reset()
obs = env.reset()
for cycle in range(nb_epoch_cycles):
# Perform rollouts.
if nenvs > 1:
# if simulating multiple envs in parallel, impossible to reset agent at the end of the episode in each
# of the environments, so resetting here instead
agent.reset()
for t_rollout in range(nb_rollout_steps):
# Predict next action.
'''no noise for test'''
action, q, _, _ = agent.step(obs, apply_noise=False, compute_Q=True)
# print('action:', action)
# Execute next action.
# if rank == 0 and render:
# env.render()
# max_action is of dimension A, whereas action is dimension (nenvs, A) - the multiplication gets broadcasted to the batch
# new_obs, r, done, info = env.step(max_action * action) # scale for execution in env (as far as DDPG is concerned, every action is in [-1, 1])
# new_obs, r, env_state,done = env.step(action, env_state)
'''actually no need for env_state: in or out'''
new_obs, r, done = env.step(action)
# print('reward:', r)
# note these outputs are batched from vecenv
# print('obs: ',obs.shape,obs, 'action: ', action.shape, action )
'''obs shape: (1,17), action shape: (1,6)'''
# print('maxaction: ', max_action.shape)
'''max_action shape: (6,) , max_action*action shape: (1,6)'''
t += 1
# if rank == 0 and render:
# env.render()
# print('r:', r)
episode_reward += r
episode_step += 1
# print('episode_re: ', episode_reward) #[1.]
# Book-keeping.
epoch_actions.append(action)
epoch_qs.append(q)
b=1.
agent.store_transition(obs, action, r, new_obs, done) #the batched data will be unrolled in memory.py's append.
# print('r: ', r)
# '''r shape: (1,)'''
obs = new_obs
# for d in range(len(done)):
# if done[d]:
# print('done')
# # Episode done.
# epoch_episode_rewards.append(episode_reward[d])
# episode_rewards_history.append(episode_reward[d])
# epoch_episode_steps.append(episode_step[d])
# episode_reward[d] = 0.
# episode_step[d] = 0
# epoch_episodes += 1
# episodes += 1
# if nenvs == 1:
# agent.reset()
'''added'''
epoch_episode_rewards.append(episode_reward)
'''
step_set.append(t)
reward_set=np.concatenate((reward_set,episode_reward))
# print(step_set,reward_set)
# print(t, episode_reward)
plt.plot(step_set,reward_set)
plt.xlabel('Steps')
plt.ylabel('Episode Reward')
plt.savefig('ddpg.png')
plt.show()
'''
episode_reward = np.zeros(nenvs, dtype = np.float32) #vector
# Train.
epoch_actor_losses = []
epoch_critic_losses = []
epoch_adaptive_distances = []
'''no training for test'''
# for t_train in range(nb_train_steps):
# Adapt param noise, if necessary. no noise for test!
# if memory.nb_entries >= batch_size and t_train % param_noise_adaption_interval == 0:
# distance = agent.adapt_param_noise()
# epoch_adaptive_distances.append(distance)
# cl, al = agent.train()
# epoch_critic_losses.append(cl)
# epoch_actor_losses.append(al)
# agent.update_target_net()
# Evaluate.
eval_episode_rewards = []
eval_qs = []
if eval_env is not None:
nenvs_eval = eval_obs.shape[0]
eval_episode_reward = np.zeros(nenvs_eval, dtype = np.float32)
for t_rollout in range(nb_eval_steps):
eval_action, eval_q, _, _ = agent.step(eval_obs, apply_noise=False, compute_Q=True)
# eval_obs, eval_r, eval_done, eval_info = eval_env.step(max_action * eval_action) # scale for execution in env (as far as DDPG is concerned, every action is in [-1, 1])
eval_obs, eval_r, eval_done, eval_info = eval_env.step( eval_action)
if render_eval:
eval_env.render()
eval_episode_reward += eval_r
eval_qs.append(eval_q)
for d in range(len(eval_done)):
if eval_done[d]:
eval_episode_rewards.append(eval_episode_reward[d])
eval_episode_rewards_history.append(eval_episode_reward[d])
eval_episode_reward[d] = 0.0
mpi_size = MPI.COMM_WORLD.Get_size()
# Log stats.
# XXX shouldn't call np.mean on variable length lists
duration = time.time() - start_time
stats = agent.get_stats()
combined_stats = stats.copy()
combined_stats['rollout/return'] = np.mean(epoch_episode_rewards)
combined_stats['rollout/return_history'] = np.mean(episode_rewards_history)
combined_stats['rollout/episode_steps'] = np.mean(epoch_episode_steps)
combined_stats['rollout/actions_mean'] = np.mean(epoch_actions)
combined_stats['rollout/Q_mean'] = np.mean(epoch_qs)
combined_stats['train/loss_actor'] = np.mean(epoch_actor_losses)
combined_stats['train/loss_critic'] = np.mean(epoch_critic_losses)
combined_stats['train/param_noise_distance'] = np.mean(epoch_adaptive_distances)
combined_stats['total/duration'] = duration
combined_stats['total/steps_per_second'] = float(t) / float(duration)
combined_stats['total/episodes'] = episodes
combined_stats['rollout/episodes'] = epoch_episodes
combined_stats['rollout/actions_std'] = np.std(epoch_actions)
mean_epoch_episode_rewards.append(np.mean(epoch_episode_rewards))
# print(step_set,mean_epoch_episode_rewards)
step_set.append(t)
plt.plot(step_set,mean_epoch_episode_rewards)
plt.xlabel('Steps')
plt.ylabel('Mean Episode Reward')
plt.savefig('ddpg_mean_test.png')
# plt.show()
# Evaluation statistics.
if eval_env is not None:
combined_stats['eval/return'] = eval_episode_rewards
combined_stats['eval/return_history'] = np.mean(eval_episode_rewards_history)
combined_stats['eval/Q'] = eval_qs
combined_stats['eval/episodes'] = len(eval_episode_rewards)
def as_scalar(x):
if isinstance(x, np.ndarray):
assert x.size == 1
return x[0]
elif np.isscalar(x):
return x
else:
raise ValueError('expected scalar, got %s'%x)
combined_stats_sums = MPI.COMM_WORLD.allreduce(np.array([ np.array(x).flatten()[0] for x in combined_stats.values()]))
combined_stats = {k : v / mpi_size for (k,v) in zip(combined_stats.keys(), combined_stats_sums)}
# Total statistics.
combined_stats['total/epochs'] = epoch + 1
combined_stats['total/steps'] = t
for key in sorted(combined_stats.keys()):
logger.record_tabular(key, combined_stats[key])
if rank == 0:
logger.dump_tabular()
logger.info('')
logdir = logger.get_dir()
if rank == 0 and logdir:
if hasattr(env, 'get_state'):
with open(os.path.join(logdir, 'env_state.pkl'), 'wb') as f:
pickle.dump(env.get_state(), f)
if eval_env and hasattr(eval_env, 'get_state'):
with open(os.path.join(logdir, 'eval_env_state.pkl'), 'wb') as f:
pickle.dump(eval_env.get_state(), f)
return agent
def run(env_id, seed, noise_type, layer_norm, evaluation, **kwargs):
# Configure things.
rank = MPI.COMM_WORLD.Get_rank()
if rank != 0:
logger.set_level(logger.DISABLED)
# Create envs.
env = gym.make(env_id)
env = Monitor(env, logger.get_dir() and os.path.join(logger.get_dir(), str(rank)))
if evaluation and rank == 0:
eval_env = gym.make(env_id)
eval_env = Monitor(eval_env, os.path.join(logger.get_dir(), 'gym_eval'))
env = Monitor(env, None)
else:
eval_env = None
# Parse noise_type
action_noise = None
param_noise = None
nb_actions = env.action_space.shape[-1]
for current_noise_type in noise_type.split(','):
current_noise_type = current_noise_type.strip()
if current_noise_type == 'none':
pass
elif 'adaptive-param' in current_noise_type:
_, stddev = current_noise_type.split('_')
param_noise = AdaptiveParamNoiseSpec(initial_stddev=float(stddev), desired_action_stddev=float(stddev))
elif 'normal' in current_noise_type:
_, stddev = current_noise_type.split('_')
action_noise = NormalActionNoise(mu=np.zeros(nb_actions), sigma=float(stddev) * np.ones(nb_actions))
elif 'ou' in current_noise_type:
_, stddev = current_noise_type.split('_')
action_noise = OrnsteinUhlenbeckActionNoise(mu=np.zeros(nb_actions),
sigma=float(stddev) * np.ones(nb_actions))
else:
raise RuntimeError('unknown noise type "{}"'.format(current_noise_type))
# Configure components.
memory = Memory(limit=int(1e6), action_shape=env.action_space.shape, observation_shape=env.observation_space.shape)
critic = Critic(layer_norm=layer_norm)
actor = Actor(nb_actions, layer_norm=layer_norm)
# Seed everything to make things reproducible.
seed = seed + 1000000 * rank
logger.info('rank {}: seed={}, logdir={}'.format(rank, seed, logger.get_dir()))
tf.reset_default_graph()
set_global_seeds(seed)
env.seed(seed)
if eval_env is not None:
eval_env.seed(seed)
# Disable logging for rank != 0 to avoid noise.
if rank == 0:
start_time = time.time()
training.train(env=env, eval_env=eval_env, param_noise=param_noise,
action_noise=action_noise, actor=actor, critic=critic, memory=memory, **kwargs)
env.close()
if eval_env is not None:
eval_env.close()
if rank == 0:
logger.info('total runtime: {}s'.format(time.time() - start_time))
def run(env_id, seed, noise_type, layer_norm, evaluation, **kwargs):
# Configure things.
rank = MPI.COMM_WORLD.Get_rank()
if rank != 0:
logger.set_level(logger.DISABLED)
# Create envs.
env = gym.make(env_id)
# ---------- AMEND: specific setting for brsEngine -----------
print("kwargs", kwargs)
env.reward_type = kwargs['reward_type']
env.set_additional_goal = kwargs['set_additional_goal']
kwargs.pop('reward_type', None)
kwargs.pop('set_additional_goal', None)
brsEngine = None
if env.reward_type == 'ttr':
if env_id == 'DubinsCarEnv-v0':
brsEngine = DubinsCar_brs_engine()
brsEngine.reset_variables()
elif env_id == 'PlanarQuadEnv-v0':
brsEngine = Quadrotor_brs_engine()
brsEngine.reset_variables()
else:
raise ValueError("invalid environment name for ttr reward!")
# You have to assign the engine!
env.brsEngine = brsEngine
# -----------------------------------------------------------
env = bench.Monitor(
env,
logger.get_dir() and os.path.join(logger.get_dir(), str(rank)))
if evaluation and rank == 0:
eval_env = gym.make(env_id)
# ---------- AMEND: specific setting for brsEngine -----------
eval_env.brsEngine = brsEngine
# ------------------------------------------------------------
eval_env = bench.Monitor(eval_env,
os.path.join(logger.get_dir(), 'gym_eval'))
env = bench.Monitor(env, None)
else:
eval_env = None
# Parse noise_type
action_noise = None
param_noise = None
nb_actions = env.action_space.shape[-1]
for current_noise_type in noise_type.split(','):
current_noise_type = current_noise_type.strip()
if current_noise_type == 'none':
pass
elif 'adaptive-param' in current_noise_type:
_, stddev = current_noise_type.split('_')
param_noise = AdaptiveParamNoiseSpec(
initial_stddev=float(stddev),
desired_action_stddev=float(stddev))
elif 'normal' in current_noise_type:
_, stddev = current_noise_type.split('_')
action_noise = NormalActionNoise(mu=np.zeros(nb_actions),
sigma=float(stddev) *
np.ones(nb_actions))
elif 'ou' in current_noise_type:
_, stddev = current_noise_type.split('_')
action_noise = OrnsteinUhlenbeckActionNoise(
mu=np.zeros(nb_actions),
sigma=float(stddev) * np.ones(nb_actions))
else:
raise RuntimeError(
'unknown noise type "{}"'.format(current_noise_type))
# Configure components.
memory = Memory(limit=int(1e6),
action_shape=env.action_space.shape,
observation_shape=env.observation_space.shape)
critic = Critic(layer_norm=layer_norm)
actor = Actor(nb_actions, layer_norm=layer_norm)
# Seed everything to make things reproducible.
seed = seed + 1000000 * rank
logger.info('rank {}: seed={}, logdir={}'.format(rank, seed,
logger.get_dir()))
tf.reset_default_graph()
set_global_seeds(seed)
env.seed(seed)
if eval_env is not None:
eval_env.seed(seed)
# Disable logging for rank != 0 to avoid noise.
if rank == 0:
start_time = time.time()
training.train(env=env,
eval_env=eval_env,
param_noise=param_noise,
action_noise=action_noise,
actor=actor,
critic=critic,
memory=memory,
**kwargs)
env.close()
if eval_env is not None:
eval_env.close()
if rank == 0:
logger.info('total runtime: {}s'.format(time.time() - start_time))
def __init__(self,
observation_shape,
action_shape,
nb_demo_kine,
nb_key_states,
batch_size=128,
noise_type='',
actor=None,
critic=None,
layer_norm=True,
observation_range=(-5., 5.),
action_range=(-1., 1.),
return_range=(-np.inf, np.inf),
normalize_returns=False,
normalize_observations=True,
reward_scale=1.,
clip_norm=None,
demo_l2_reg=0.,
critic_l2_reg=0.,
actor_lr=1e-4,
critic_lr=1e-3,
demo_lr=5e-3,
gamma=0.99,
tau=0.001,
enable_popart=False,
save_ckpt=True):
# Noise
nb_actions = action_shape[-1]
param_noise, action_noise = process_noise_type(noise_type, nb_actions)
logger.info('param_noise', param_noise)
logger.info('action_noise', action_noise)
# States recording
self.memory = Memory(limit=int(2e5),
action_shape=action_shape,
observation_shape=observation_shape)
# Models
self.nb_demo_kine = nb_demo_kine
self.actor = actor or Actor(
nb_actions, nb_demo_kine, layer_norm=layer_norm)
self.nb_key_states = nb_key_states
self.critic = critic or Critic(nb_key_states, layer_norm=layer_norm)
self.nb_obs_org = nb_key_states
# Inputs.
self.obs0 = tf.placeholder(tf.float32,
shape=(None, ) + observation_shape,
name='obs0')
self.obs1 = tf.placeholder(tf.float32,
shape=(None, ) + observation_shape,
name='obs1')
self.terminals1 = tf.placeholder(tf.float32,
shape=(None, 1),
name='terminals1')
self.rewards = tf.placeholder(tf.float32,
shape=(None, 1),
name='rewards')
self.actions = tf.placeholder(tf.float32,
shape=(None, ) + action_shape,
name='actions')
# self.critic_target_Q: value assigned by self.target_Q_obs0
self.critic_target_Q = tf.placeholder(tf.float32,
shape=(None, 1),
name='critic_target_Q')
self.param_noise_stddev = tf.placeholder(tf.float32,
shape=(),
name='param_noise_stddev')
# change in observations
self.obs_delta_kine = (self.obs1 - self.obs0)[:, :self.nb_demo_kine]
self.obs_delta_kstates = (self.obs1 -
self.obs0)[:, :self.nb_key_states]
# Parameters.
self.gamma = gamma
self.tau = tau
self.normalize_observations = normalize_observations
self.normalize_returns = normalize_returns
self.action_noise = action_noise
self.param_noise = param_noise
self.action_range = action_range
self.return_range = return_range
self.observation_range = observation_range
self.actor_lr = actor_lr
self.critic_lr = critic_lr
self.demo_lr = demo_lr
self.clip_norm = clip_norm
self.enable_popart = enable_popart
self.reward_scale = reward_scale
self.batch_size = batch_size
self.stats_sample = None
self.critic_l2_reg = critic_l2_reg
self.demo_l2_reg = demo_l2_reg
# Observation normalization.
if self.normalize_observations:
with tf.variable_scope('obs_rms'):
self.obs_rms = RunningMeanStd(shape=observation_shape)
else:
self.obs_rms = None
self.normalized_obs0 = tf.clip_by_value(
obs_norm_partial(self.obs0, self.obs_rms, self.nb_obs_org),
self.observation_range[0], self.observation_range[1])
normalized_obs1 = tf.clip_by_value(
obs_norm_partial(self.obs1, self.obs_rms, self.nb_obs_org),
self.observation_range[0], self.observation_range[1])
# Return normalization.
if self.normalize_returns:
with tf.variable_scope('ret_rms'):
self.ret_rms = RunningMeanStd()
else:
self.ret_rms = None
# Create target networks.
target_actor = copy(self.actor)
target_actor.name = 'target_actor'
self.target_actor = target_actor
target_critic = copy(self.critic)
target_critic.name = 'target_critic'
self.target_critic = target_critic
# Create networks and core TF parts that are shared across set-up parts.
# the actor output is [0,1], need to normalised to [-1,1] before feeding into critic
self.actor_tf, self.demo_aprx = self.actor(self.normalized_obs0)
# critic loss
# normalized_critic_tf, pred_rwd, pred_obs_delta: critic_loss
self.normalized_critic_tf, self.pred_rwd, self.pred_obs_delta = self.critic(
self.normalized_obs0, act_norm(self.actions))
# self.critic_tf: only in logging [reference_Q_mean/std]
self.critic_tf = ret_denormalize(
tf.clip_by_value(self.normalized_critic_tf, self.return_range[0],
self.return_range[1]), self.ret_rms)
# actor loss
normalized_critic_with_actor_tf = self.critic(self.normalized_obs0,
act_norm(self.actor_tf),
reuse=True)[0]
# self.critic_with_actor_tf: actor loss, and logging [reference_Q_tf_mean/std]
self.critic_with_actor_tf = ret_denormalize(
tf.clip_by_value(normalized_critic_with_actor_tf,
self.return_range[0], self.return_range[1]),
self.ret_rms)
# target Q
self.target_action = tf.clip_by_value(
target_actor(normalized_obs1)[0], self.action_range[0],
self.action_range[1])
self.target_Q_obs1 = ret_denormalize(
target_critic(normalized_obs1, act_norm(self.target_action))[0],
self.ret_rms)
self.target_Q_obs0 = self.rewards + (
1. - self.terminals1) * gamma * self.target_Q_obs1
# Set up parts.
if self.param_noise is not None:
self.setup_param_noise(self.normalized_obs0)
self.setup_actor_optimizer()
self.setup_critic_optimizer()
if self.normalize_returns and self.enable_popart:
self.setup_popart()
self.setup_stats()
self.setup_target_network_updates()
self.dbg_vars = self.actor.dbg_vars + self.critic.dbg_vars
self.sess = None
# Set up checkpoint saver
self.save_ckpt = save_ckpt
if save_ckpt:
self.saver = tf.train.Saver(tf.global_variables(), max_to_keep=20)
else:
# saver for loading ckpt
self.saver = tf.train.Saver()
self.main_summaries = tf.summary.merge_all()
logdir = logger.get_dir()
if logdir:
self.train_writer = tf.summary.FileWriter(
os.path.join(logdir, 'tb'), tf.get_default_graph())
else:
self.train_writer = None
def learn(
save_path,
network,
env,
seed=None,
total_timesteps=None,
nb_epochs=None, # with default settings, perform 1M steps total
nb_epoch_cycles=7, #50
nb_rollout_steps=3, #100
reward_scale=1.0,
render=False,
render_eval=False,
# noise_type='adaptive-param_0.2',
# noise_type='normal_0.2', # large noise
# noise_type='normal_0.02', # small noise
noise_type='normal_2.0',
# action ranges 360, so noise scale should be chosen properly
# noise_type='normal_5', # large noise
# noise_type='normal_0.2', # small noise
# noise_type='normal_0.00001', # no noise
# noise_type='ou_0.9',
normalize_returns=False,
normalize_observations=True,
critic_l2_reg=1e-2,
actor_lr=1e-4, # large lr
critic_lr=1e-3, # large lr
# actor_lr=1e-7, # small lr
# critic_lr=1e-3, # small lr
# actor_lr = 1e-10, # no lr
# critic_lr=1e-10, # no lr
popart=False,
gamma=0.99,
clip_norm=None,
nb_train_steps=3, # per epoch cycle and MPI worker, 50
nb_eval_steps=1, #100
batch_size=640, # per MPI worker
tau=0.01,
eval_env=None,
param_noise_adaption_interval=3, #50
**network_kwargs):
if total_timesteps is not None:
assert nb_epochs is None
nb_epochs = int(total_timesteps) // (nb_epoch_cycles *
nb_rollout_steps)
else:
nb_epochs = 500
rank = MPI.COMM_WORLD.Get_rank()
nb_actions = env.num_actions
action_shape = np.array(nb_actions * [0]).shape
#4 pairs pos + 3 link length
# nb_features = 2*(env.num_actions+1)+env.num_actions
#4 pairs pos + 1 pair target pos
nb_features = 2 * (env.num_actions + 2)
observation_shape = np.array(nb_features * [0]).shape
# assert (np.abs(env.action_space.low) == env.action_space.high).all() # we assume symmetric actions.
# memory = Memory(limit=int(1e6), action_shape=env.action_space.shape, observation_shape=env.observation_space.shape)
memory = Memory(limit=int(1e6),
action_shape=action_shape,
observation_shape=observation_shape)
critic = Critic(network=network, **network_kwargs)
actor = Actor(nb_actions, network=network, **network_kwargs)
action_noise = None
param_noise = None
# nb_actions = env.action_space.shape[-1]
if noise_type is not None:
for current_noise_type in noise_type.split(','):
current_noise_type = current_noise_type.strip()
if current_noise_type == 'none':
pass
elif 'adaptive-param' in current_noise_type:
_, stddev = current_noise_type.split('_')
param_noise = AdaptiveParamNoiseSpec(
initial_stddev=float(stddev),
desired_action_stddev=float(stddev))
elif 'normal' in current_noise_type:
_, stddev = current_noise_type.split('_')
action_noise = NormalActionNoise(mu=np.zeros(nb_actions),
sigma=float(stddev) *
np.ones(nb_actions))
elif 'ou' in current_noise_type:
_, stddev = current_noise_type.split('_')
action_noise = OrnsteinUhlenbeckActionNoise(
mu=np.zeros(nb_actions),
sigma=float(stddev) * np.ones(nb_actions))
else:
raise RuntimeError(
'unknown noise type "{}"'.format(current_noise_type))
# max_action = env.action_space.high
# logger.info('scaling actions by {} before executing in env'.format(max_action))
# agent = DDPG(actor, critic, memory, env.observation_space.shape, env.action_space.shape,
agent = DDPG(actor,
critic,
memory,
observation_shape,
action_shape,
gamma=gamma,
tau=tau,
normalize_returns=normalize_returns,
normalize_observations=normalize_observations,
batch_size=batch_size,
action_noise=action_noise,
param_noise=param_noise,
critic_l2_reg=critic_l2_reg,
actor_lr=actor_lr,
critic_lr=critic_lr,
enable_popart=popart,
clip_norm=clip_norm,
reward_scale=reward_scale)
logger.info('Using agent with the following configuration:')
logger.info(str(agent.__dict__.items()))
eval_episode_rewards_history = deque(maxlen=100)
episode_rewards_history = deque(maxlen=100)
sess = U.get_session()
# Prepare everything.
agent.initialize(sess)
# sess.graph.finalize()
agent.reset()
obs = env.reset()
if eval_env is not None:
eval_obs = eval_env.reset()
nenvs = obs.shape[0]
episode_reward = np.zeros(nenvs, dtype=np.float32) #vector
episode_step = np.zeros(nenvs, dtype=int) # vector
episodes = 0 #scalar
t = 0 # scalar
step_set = []
reward_set = []
epoch = 0
start_time = time.time()
epoch_episode_rewards = []
mean_epoch_episode_rewards = []
epoch_episode_steps = []
epoch_actions = []
epoch_qs = []
episode_end_distance = []
epoch_episodes = 0
SPARSE_REWARD = False
'''add this line to make non-initialized to be initialized'''
agent.load_ini(sess, save_path)
for epoch in range(nb_epochs):
print('epochs: ', epoch)
obs = env.reset()
agent.save(save_path)
epoch_episode_rewards = []
for cycle in range(nb_epoch_cycles):
# Perform rollouts.
if nenvs > 1:
# if simulating multiple envs in parallel, impossible to reset agent at the end of the episode in each
# of the environments, so resetting here instead
agent.reset()
for t_rollout in range(nb_rollout_steps):
# Predict next action.
action, q, _, _ = agent.step(obs,
apply_noise=True,
compute_Q=True)
# print('action:', action)
if SPARSE_REWARD:
new_obs, r, done, end_distance = env.step(
action, SPARSE_REWARD)
else:
new_obs, r, done = env.step(action, SPARSE_REWARD)
t += 1
episode_reward += r
episode_step += 1
# print('episode_re: ', episode_reward) #[1.]
# Book-keeping.
epoch_actions.append(action)
epoch_qs.append(q)
b = 1.
agent.store_transition(
obs, action, r, new_obs, done
) #the batched data will be unrolled in memory.py's append.
# print('r: ', r)
# '''r shape: (1,)'''
obs = new_obs
epoch_episode_rewards.append(episode_reward)
if cycle == nb_epoch_cycles - 1:
# record the distance from the end position of reacher to the goal for the last step of each episode
if SPARSE_REWARD:
episode_end_distance.append(end_distance)
else:
end_distance = 100.0 / r - 1
episode_end_distance.append(end_distance[0])
episode_reward = np.zeros(nenvs, dtype=np.float32) #vector
# Train.
epoch_actor_losses = []
epoch_critic_losses = []
epoch_adaptive_distances = []
# filling memory with noised initialized policy & preupdate the critic networks
preheating_step = 30 #50 episode = 600 steps, 12 steps per episode
if epoch > preheating_step:
# print('memory_entries: ',memory.nb_entries)
for t_train in range(nb_train_steps):
# Adapt param noise, if necessary.
if memory.nb_entries >= batch_size and t_train % param_noise_adaption_interval == 0:
distance = agent.adapt_param_noise()
epoch_adaptive_distances.append(distance)
# print('Train!')
cl, al = agent.train()
epoch_critic_losses.append(cl)
epoch_actor_losses.append(al)
agent.update_target_net()
else:
# update two critic networks at start
cl = agent.update_critic()
epoch_critic_losses.append(cl)
print('critic loss in initial training: ', cl)
pass
# Evaluate.
eval_episode_rewards = []
eval_qs = []
if eval_env is not None:
nenvs_eval = eval_obs.shape[0]
eval_episode_reward = np.zeros(nenvs_eval, dtype=np.float32)
for t_rollout in range(nb_eval_steps):
eval_action, eval_q, _, _ = agent.step(eval_obs,
apply_noise=False,
compute_Q=True)
# eval_obs, eval_r, eval_done, eval_info = eval_env.step(max_action * eval_action) # scale for execution in env (as far as DDPG is concerned, every action is in [-1, 1])
eval_obs, eval_r, eval_done, eval_info = eval_env.step(
eval_action)
if render_eval:
eval_env.render()
eval_episode_reward += eval_r
eval_qs.append(eval_q)
for d in range(len(eval_done)):
if eval_done[d]:
eval_episode_rewards.append(eval_episode_reward[d])
eval_episode_rewards_history.append(
eval_episode_reward[d])
eval_episode_reward[d] = 0.0
mpi_size = MPI.COMM_WORLD.Get_size()
# Log stats.
# XXX shouldn't call np.mean on variable length lists
duration = time.time() - start_time
stats = agent.get_stats()
combined_stats = stats.copy()
combined_stats['rollout/return'] = np.mean(epoch_episode_rewards)
combined_stats['rollout/return_history'] = np.mean(
episode_rewards_history)
combined_stats['rollout/episode_steps'] = np.mean(epoch_episode_steps)
combined_stats['rollout/actions_mean'] = np.mean(epoch_actions)
combined_stats['rollout/Q_mean'] = np.mean(epoch_qs)
combined_stats['train/loss_actor'] = np.mean(epoch_actor_losses)
combined_stats['train/loss_critic'] = np.mean(epoch_critic_losses)
combined_stats['train/param_noise_distance'] = np.mean(
epoch_adaptive_distances)
combined_stats['total/duration'] = duration
combined_stats['total/steps_per_second'] = float(t) / float(duration)
combined_stats['total/episodes'] = episodes
combined_stats['rollout/episodes'] = epoch_episodes
combined_stats['rollout/actions_std'] = np.std(epoch_actions)
mean_epoch_episode_rewards.append(np.mean(epoch_episode_rewards))
# print(step_set,mean_epoch_episode_rewards)
step_set.append(t)
plt.figure(1)
plt.plot(step_set, mean_epoch_episode_rewards)
plt.xlabel('Steps')
plt.ylabel('Mean Episode Reward')
plt.savefig('ddpg_mean.png')
plt.figure(2)
plt.plot(step_set, episode_end_distance)
plt.xlabel('Steps')
plt.ylabel('Distance to Target')
plt.savefig('ddpgini_distance.png')
# plt.show()
# Evaluation statistics.
if eval_env is not None:
combined_stats['eval/return'] = eval_episode_rewards
combined_stats['eval/return_history'] = np.mean(
eval_episode_rewards_history)
combined_stats['eval/Q'] = eval_qs
combined_stats['eval/episodes'] = len(eval_episode_rewards)
def as_scalar(x):
if isinstance(x, np.ndarray):
assert x.size == 1
return x[0]
elif np.isscalar(x):
return x
else:
raise ValueError('expected scalar, got %s' % x)
combined_stats_sums = MPI.COMM_WORLD.allreduce(
np.array(
[np.array(x).flatten()[0] for x in combined_stats.values()]))
combined_stats = {
k: v / mpi_size
for (k, v) in zip(combined_stats.keys(), combined_stats_sums)
}
# Total statistics.
combined_stats['total/epochs'] = epoch + 1
combined_stats['total/steps'] = t
for key in sorted(combined_stats.keys()):
logger.record_tabular(key, combined_stats[key])
if rank == 0:
logger.dump_tabular()
logger.info('')
logdir = logger.get_dir()
if rank == 0 and logdir:
if hasattr(env, 'get_state'):
with open(os.path.join(logdir, 'env_state.pkl'), 'wb') as f:
pickle.dump(env.get_state(), f)
if eval_env and hasattr(eval_env, 'get_state'):
with open(os.path.join(logdir, 'eval_env_state.pkl'),
'wb') as f:
pickle.dump(eval_env.get_state(), f)
print('stepset: ', step_set)
print('rewards: ', mean_epoch_episode_rewards)
print('distances: ', episode_end_distance)
return agent
class DDPGAgent:
def __init__(self,
env,
gamma,
tau,
buffer_maxlen,
critic_learning_rate,
actor_learning_rate,
max_action=1):
self.device = torch.device(
"cuda" if torch.cuda.is_available() else "cpu")
self.env = env
self.obs_dim = env.observation_space.shape[0]
self.action_dim = env.action_space.shape[0]
self.noise = OUNoise(env.action_space)
self.iter = 0.0
self.noisy = False
self.max_action = max_action
print(self.action_dim)
print(self.obs_dim)
# RL hyperparameters
self.env = env
self.gamma = gamma
self.tau = tau
# Initialize critic and actorr networks
self.critic = Critic(self.obs_dim, self.action_dim).to(self.device)
self.critic_target = Critic(self.obs_dim,
self.action_dim).to(self.device)
self.actor = Actor(self.obs_dim, self.action_dim,
self.max_action).to(self.device)
self.actor_target = Actor(self.obs_dim,
self.action_dim).to(self.device)
# Copy target network paramters for critic
for target_param, param in zip(self.critic_target.parameters(),
self.critic.parameters()):
target_param.data.copy_(param.data)
# Set Optimization algorithms
self.critic_optimizer = optim.Adam(self.critic.parameters(),
lr=critic_learning_rate)
self.actor_optimizer = optim.Adam(self.actor.parameters(),
lr=actor_learning_rate)
self.replay_buffer = ExperienceReplayLog(buffer_maxlen)
def get_action(self, obs):
#print('obs;',obs)
if self.noisy == True:
state = torch.FloatTensor(obs).unsqueeze(0).to(self.device)
action = self.actor.forward(state)
action = action.squeeze(0).cpu().detach().numpy()
action = self.noise.get_action(action, self.iter)
self.iter = self.iter + 1
else:
state = torch.FloatTensor(obs).unsqueeze(0).to(self.device)
action = self.actor.forward(state)
action = action.squeeze(0).cpu().detach().numpy()
return action
def update(self, batch_size):
#Batch updates
states, actions, rewards, next_states, _ = self.replay_buffer.sample(
batch_size)
state_batch, action_batch, reward_batch, next_state_batch, masks = self.replay_buffer.sample(
batch_size)
state_batch = torch.FloatTensor(state_batch).to(self.device)
action_batch = torch.FloatTensor(action_batch).to(self.device)
reward_batch = torch.FloatTensor(reward_batch).to(self.device)
next_state_batch = torch.FloatTensor(next_state_batch).to(self.device)
masks = torch.FloatTensor(masks).to(self.device)
# Q info updates
curr_Q = self.critic.forward(state_batch, action_batch)
next_actions = self.actor_target.forward(next_state_batch)
next_Q = self.critic_target.forward(next_state_batch,
next_actions.detach())
expected_Q = reward_batch + self.gamma * next_Q
# Update Critic network
q_loss = F.mse_loss(curr_Q, expected_Q.detach())
self.critic_optimizer.zero_grad()
q_loss.backward()
self.critic_optimizer.step()
# Update Actor network
policy_loss = -self.critic.forward(
state_batch, self.actor.forward(state_batch)).mean()
self.actor_optimizer.zero_grad()
policy_loss.backward()
self.actor_optimizer.step()
# Update Actor and Critic target networks
for target_param, param in zip(self.actor_target.parameters(),
self.actor.parameters()):
target_param.data.copy_(param.data * self.tau + target_param.data *
(1.0 - self.tau))
for target_param, param in zip(self.critic_target.parameters(),
self.critic.parameters()):
target_param.data.copy_(param.data * self.tau + target_param.data *
(1.0 - self.tau))