def env_step(env, action):
# action should be of size: batch x 1
action = ptu.get_numpy(action.squeeze(dim=-1))
next_obs, reward, done, info = env.step(action)
# move to torch
next_obs = ptu.from_numpy(next_obs).view(-1, next_obs.shape[0])
reward = ptu.FloatTensor([reward]).view(-1, 1)
done = ptu.from_numpy(np.array(done, dtype=int)).view(-1, 1)
return next_obs, reward, done, info
def collect_rollouts_per_task(task_idx, agent, policy_storage, env,
num_rollouts):
for rollout in range(num_rollouts):
obs = ptu.from_numpy(env.reset(task_idx))
obs = obs.reshape(-1, obs.shape[-1])
done_rollout = False
while not done_rollout:
action, _, _, _ = agent.act(obs=obs) # SAC
# observe reward and next obs
next_obs, reward, done, info = utl.env_step(
env, action.squeeze(dim=0))
done_rollout = False if ptu.get_numpy(done[0][0]) == 0. else True
# add data to policy buffer - (s+, a, r, s'+, term)
term = env.unwrapped.is_goal_state() if "is_goal_state" in dir(
env.unwrapped) else False
rew_to_buffer = ptu.get_numpy(reward.squeeze(dim=0))
policy_storage.add_sample(
task=0, #task_idx,
observation=ptu.get_numpy(obs.squeeze(dim=0)),
action=ptu.get_numpy(action.squeeze(dim=0)),
reward=rew_to_buffer,
terminal=np.array([term], dtype=float),
next_observation=ptu.get_numpy(next_obs.squeeze(dim=0)))
# set: obs <- next_obs
obs = next_obs.clone()
def evaluate(self, tasks):
num_episodes = self.args.max_rollouts_per_task
num_steps_per_episode = self.env.unwrapped._max_episode_steps
returns_per_episode = np.zeros((len(tasks), num_episodes))
success_rate = np.zeros(len(tasks))
if self.args.policy == 'dqn':
values = np.zeros((len(tasks), self.args.max_trajectory_len))
else:
obs_size = self.env.unwrapped.observation_space.shape[0]
observations = np.zeros((len(tasks), self.args.max_trajectory_len + 1, obs_size))
log_probs = np.zeros((len(tasks), self.args.max_trajectory_len))
for task_idx, task in enumerate(tasks):
obs = ptu.from_numpy(self.env.reset(task))
obs = obs.reshape(-1, obs.shape[-1])
step = 0
if self.args.policy == 'sac':
observations[task_idx, step, :] = ptu.get_numpy(obs[0, :obs_size])
for episode_idx in range(num_episodes):
running_reward = 0.
for step_idx in range(num_steps_per_episode):
# add distribution parameters to observation - policy is conditioned on posterior
if self.args.policy == 'dqn':
action, value = self.agent.act(obs=obs, deterministic=True)
else:
action, _, _, log_prob = self.agent.act(obs=obs,
deterministic=self.args.eval_deterministic,
return_log_prob=True)
# observe reward and next obs
next_obs, reward, done, info = utl.env_step(self.env, action.squeeze(dim=0))
running_reward += reward.item()
if self.args.policy == 'dqn':
values[task_idx, step] = value.item()
else:
observations[task_idx, step + 1, :] = ptu.get_numpy(next_obs[0, :obs_size])
log_probs[task_idx, step] = ptu.get_numpy(log_prob[0])
if "is_goal_state" in dir(self.env.unwrapped) and self.env.unwrapped.is_goal_state():
success_rate[task_idx] = 1.
# set: obs <- next_obs
obs = next_obs.clone()
step += 1
returns_per_episode[task_idx, episode_idx] = running_reward
if self.args.policy == 'dqn':
return returns_per_episode, success_rate, values
else:
return returns_per_episode, success_rate, log_probs, observations
def collect_rollouts(self, num_rollouts, random_actions=False):
'''
:param num_rollouts:
:param random_actions: whether to use policy to sample actions, or randomly sample action space
:return:
'''
for rollout in range(num_rollouts):
obs = ptu.from_numpy(self.env.reset(self.task_idx))
obs = obs.reshape(-1, obs.shape[-1])
done_rollout = False
while not done_rollout:
if random_actions:
if self.args.policy == 'dqn':
action = ptu.FloatTensor([[[self.env.action_space.sample()]]]).long() # Sample random action
else:
action = ptu.FloatTensor([self.env.action_space.sample()]) # Sample random action
else:
if self.args.policy == 'dqn':
action, _ = self.agent.act(obs=obs) # DQN
else:
action, _, _, _ = self.agent.act(obs=obs) # SAC
# observe reward and next obs
next_obs, reward, done, info = utl.env_step(self.env, action.squeeze(dim=0))
done_rollout = False if ptu.get_numpy(done[0][0]) == 0. else True
# add data to policy buffer - (s+, a, r, s'+, term)
term = self.env.unwrapped.is_goal_state() if "is_goal_state" in dir(self.env.unwrapped) else False
if self.args.dense_train_sparse_test:
rew_to_buffer = {rew_type: rew for rew_type, rew in info.items()
if rew_type.startswith('reward')}
else:
rew_to_buffer = ptu.get_numpy(reward.squeeze(dim=0))
self.policy_storage.add_sample(task=self.task_idx,
observation=ptu.get_numpy(obs.squeeze(dim=0)),
action=ptu.get_numpy(action.squeeze(dim=0)),
reward=rew_to_buffer,
terminal=np.array([term], dtype=float),
next_observation=ptu.get_numpy(next_obs.squeeze(dim=0)))
# set: obs <- next_obs
obs = next_obs.clone()
# update statistics
self._n_env_steps_total += 1
if "is_goal_state" in dir(self.env.unwrapped) and self.env.unwrapped.is_goal_state(): # count successes
self._successes_in_buffer += 1
self._n_rollouts_total += 1
def evaluate(self):
num_episodes = self.args.max_rollouts_per_task
num_steps_per_episode = self.env.unwrapped._max_episode_steps
num_tasks = self.args.num_eval_tasks
obs_size = self.env.unwrapped.observation_space.shape[0]
returns_per_episode = np.zeros((num_tasks, num_episodes))
success_rate = np.zeros(num_tasks)
rewards = np.zeros((num_tasks, self.args.trajectory_len))
reward_preds = np.zeros((num_tasks, self.args.trajectory_len))
observations = np.zeros(
(num_tasks, self.args.trajectory_len + 1, obs_size))
if self.args.policy == 'sac':
log_probs = np.zeros((num_tasks, self.args.trajectory_len))
# This part is very specific for the Semi-Circle env
# if self.args.env_name == 'PointRobotSparse-v0':
# reward_belief = np.zeros((num_tasks, self.args.trajectory_len))
#
# low_x, high_x, low_y, high_y = -2., 2., -1., 2.
# resolution = 0.1
# grid_x = np.arange(low_x, high_x + resolution, resolution)
# grid_y = np.arange(low_y, high_y + resolution, resolution)
# centers_x = (grid_x[:-1] + grid_x[1:]) / 2
# centers_y = (grid_y[:-1] + grid_y[1:]) / 2
# yv, xv = np.meshgrid(centers_y, centers_x, sparse=False, indexing='ij')
# centers = np.vstack([xv.ravel(), yv.ravel()]).T
# n_grid_points = centers.shape[0]
# reward_belief_discretized = np.zeros((num_tasks, self.args.trajectory_len, centers.shape[0]))
for task_loop_i, task in enumerate(
self.env.unwrapped.get_all_eval_task_idx()):
obs = ptu.from_numpy(self.env.reset(task))
obs = obs.reshape(-1, obs.shape[-1])
step = 0
# get prior parameters
with torch.no_grad():
task_sample, task_mean, task_logvar, hidden_state = self.vae.encoder.prior(
batch_size=1)
observations[task_loop_i,
step, :] = ptu.get_numpy(obs[0, :obs_size])
for episode_idx in range(num_episodes):
running_reward = 0.
for step_idx in range(num_steps_per_episode):
# add distribution parameters to observation - policy is conditioned on posterior
augmented_obs = self.get_augmented_obs(
obs, task_mean, task_logvar)
if self.args.policy == 'dqn':
action, value = self.agent.act(obs=augmented_obs,
deterministic=True)
else:
action, _, _, log_prob = self.agent.act(
obs=augmented_obs,
deterministic=self.args.eval_deterministic,
return_log_prob=True)
# observe reward and next obs
next_obs, reward, done, info = utl.env_step(
self.env, action.squeeze(dim=0))
running_reward += reward.item()
# done_rollout = False if ptu.get_numpy(done[0][0]) == 0. else True
# update encoding
task_sample, task_mean, task_logvar, hidden_state = self.update_encoding(
obs=next_obs,
action=action,
reward=reward,
done=done,
hidden_state=hidden_state)
rewards[task_loop_i, step] = reward.item()
reward_preds[task_loop_i, step] = ptu.get_numpy(
self.vae.reward_decoder(task_sample, next_obs, obs,
action)[0, 0])
# This part is very specific for the Semi-Circle env
# if self.args.env_name == 'PointRobotSparse-v0':
# reward_belief[task, step] = ptu.get_numpy(
# self.vae.compute_belief_reward(task_mean, task_logvar, obs, next_obs, action)[0])
#
# reward_belief_discretized[task, step, :] = ptu.get_numpy(
# self.vae.compute_belief_reward(task_mean.repeat(n_grid_points, 1),
# task_logvar.repeat(n_grid_points, 1),
# None,
# torch.cat((ptu.FloatTensor(centers),
# ptu.zeros(centers.shape[0], 1)), dim=-1).unsqueeze(0),
# None)[:, 0])
observations[task_loop_i, step + 1, :] = ptu.get_numpy(
next_obs[0, :obs_size])
if self.args.policy != 'dqn':
log_probs[task_loop_i,
step] = ptu.get_numpy(log_prob[0])
if "is_goal_state" in dir(
self.env.unwrapped
) and self.env.unwrapped.is_goal_state():
success_rate[task_loop_i] = 1.
# set: obs <- next_obs
obs = next_obs.clone()
step += 1
returns_per_episode[task_loop_i, episode_idx] = running_reward
if self.args.policy == 'dqn':
return returns_per_episode, success_rate, observations, rewards, reward_preds
# This part is very specific for the Semi-Circle env
# elif self.args.env_name == 'PointRobotSparse-v0':
# return returns_per_episode, success_rate, log_probs, observations, \
# rewards, reward_preds, reward_belief, reward_belief_discretized, centers
else:
return returns_per_episode, success_rate, log_probs, observations, rewards, reward_preds
def rollout_policy(env, learner):
is_vae_exist = "vae" in dir(learner)
observations = []
actions = []
rewards = []
values = []
if is_vae_exist:
latent_samples = []
latent_means = []
latent_logvars = []
obs = ptu.from_numpy(env.reset())
obs = obs.reshape(-1, obs.shape[-1])
observations.append(obs)
done_rollout = False
if is_vae_exist:
# get prior parameters
with torch.no_grad():
task_sample, task_mean, task_logvar, hidden_state = learner.vae.encoder.prior(
batch_size=1)
# store
latent_samples.append(ptu.get_numpy(task_sample[0, 0]))
latent_means.append(ptu.get_numpy(task_mean[0, 0]))
latent_logvars.append(ptu.get_numpy(task_logvar[0, 0]))
while not done_rollout:
if is_vae_exist:
# add distribution parameters to observation - policy is conditioned on posterior
augmented_obs = learner.get_augmented_obs(obs=obs,
task_mu=task_mean,
task_std=task_logvar)
with torch.no_grad():
action, value = learner.agent.act(obs=augmented_obs,
deterministic=True)
else:
action, _, _, _ = learner.agent.act(obs=obs)
# observe reward and next obs
next_obs, reward, done, info = utl.env_step(env, action.squeeze(dim=0))
# store
observations.append(next_obs)
actions.append(action)
values.append(value)
rewards.append(reward.item())
done_rollout = False if ptu.get_numpy(done[0][0]) == 0. else True
if is_vae_exist:
# update encoding
task_sample, task_mean, task_logvar, hidden_state = learner.vae.encoder(
action,
next_obs,
reward.reshape((1, 1)),
hidden_state,
return_prior=False)
# values.append(value.item())
latent_samples.append(ptu.get_numpy(task_sample[0]))
latent_means.append(ptu.get_numpy(task_mean[0]))
latent_logvars.append(ptu.get_numpy(task_logvar[0]))
# set: obs <- next_obs
obs = next_obs.clone()
if is_vae_exist:
return observations, actions, rewards, values, \
latent_samples, latent_means, latent_logvars
else:
return observations, actions, rewards, values
def collect_rollouts(self, num_rollouts, random_actions=False):
'''
:param num_rollouts:
:param random_actions: whether to use policy to sample actions, or randomly sample action space
:return:
'''
for rollout in range(num_rollouts):
obs = ptu.from_numpy(self.env.reset(self.task_idx))
obs = obs.reshape(-1, obs.shape[-1])
done_rollout = False
# self.policy_storage.reset_running_episode(self.task_idx)
# if self.args.fixed_latent_params:
# assert 2 ** self.args.task_embedding_size >= self.args.num_tasks
# task_mean = ptu.FloatTensor(utl.vertices(self.args.task_embedding_size)[self.task_idx])
# task_logvar = -2. * ptu.ones_like(task_logvar) # arbitrary negative enough number
# add distribution parameters to observation - policy is conditioned on posterior
augmented_obs = self.get_augmented_obs(obs=obs)
while not done_rollout:
if random_actions:
if self.args.policy == 'dqn':
action = ptu.FloatTensor([[
self.env.action_space.sample()
]]).type(torch.long) # Sample random action
else:
action = ptu.FloatTensor(
[self.env.action_space.sample()])
else:
if self.args.policy == 'dqn':
action, _ = self.agent.act(obs=augmented_obs) # DQN
else:
action, _, _, _ = self.agent.act(
obs=augmented_obs) # SAC
# observe reward and next obs
next_obs, reward, done, info = utl.env_step(
self.env, action.squeeze(dim=0))
done_rollout = False if ptu.get_numpy(
done[0][0]) == 0. else True
# get augmented next obs
augmented_next_obs = self.get_augmented_obs(obs=next_obs)
# add data to policy buffer - (s+, a, r, s'+, term)
term = self.env.unwrapped.is_goal_state(
) if "is_goal_state" in dir(self.env.unwrapped) else False
self.policy_storage.add_sample(
task=self.task_idx,
observation=ptu.get_numpy(augmented_obs.squeeze(dim=0)),
action=ptu.get_numpy(action.squeeze(dim=0)),
reward=ptu.get_numpy(reward.squeeze(dim=0)),
terminal=np.array([term], dtype=float),
next_observation=ptu.get_numpy(
augmented_next_obs.squeeze(dim=0)))
if not random_actions:
self.current_experience_storage.add_sample(
task=self.task_idx,
observation=ptu.get_numpy(
augmented_obs.squeeze(dim=0)),
action=ptu.get_numpy(action.squeeze(dim=0)),
reward=ptu.get_numpy(reward.squeeze(dim=0)),
terminal=np.array([term], dtype=float),
next_observation=ptu.get_numpy(
augmented_next_obs.squeeze(dim=0)))
# set: obs <- next_obs
obs = next_obs.clone()
augmented_obs = augmented_next_obs.clone()
# update statistics
self._n_env_steps_total += 1
if "is_goal_state" in dir(
self.env.unwrapped
) and self.env.unwrapped.is_goal_state(): # count successes
self._successes_in_buffer += 1
self._n_rollouts_total += 1
def collect_rollouts(self):
self.training_mode(False)
num_episodes = self.args.max_rollouts_per_task
num_steps_per_episode = self.env.unwrapped._max_episode_steps
num_tasks = self.args.num_eval_tasks
obs_size = self.env.unwrapped.observation_space.shape[0]
returns_per_episode = np.zeros((num_tasks, num_episodes))
success_rate = np.zeros(num_tasks)
rewards = np.zeros((num_tasks, self.args.trajectory_len))
observations = np.zeros(
(num_tasks, self.args.trajectory_len + 1, obs_size))
actions = np.zeros(
(num_tasks, self.args.trajectory_len, self.args.action_dim))
log_probs = np.zeros((num_tasks, self.args.trajectory_len))
for task in self.env.unwrapped.get_all_task_idx():
obs = ptu.from_numpy(self.env.reset(task))
obs = obs.reshape(-1, obs.shape[-1])
step = 0
# get prior parameters
task_sample, task_mean, task_logvar, hidden_state = self.vae.encoder.prior(
batch_size=1)
observations[task, step, :] = ptu.get_numpy(obs[0, :obs_size])
for episode_idx in range(num_episodes):
running_reward = 0.
for step_idx in range(num_steps_per_episode):
# add distribution parameters to observation - policy is conditioned on posterior
augmented_obs = self.get_augmented_obs(
obs, task_mean, task_logvar)
action, _, _, log_prob = self.agent.act(
obs=augmented_obs,
deterministic=self.args.eval_deterministic,
return_log_prob=True)
# observe reward and next obs
next_obs, reward, done, info = utl.env_step(
self.env, action.squeeze(dim=0))
running_reward += reward.item()
# update encoding
task_sample, task_mean, task_logvar, hidden_state = self.update_encoding(
obs=next_obs,
action=action,
reward=reward,
done=done,
hidden_state=hidden_state)
rewards[task, step] = reward.item()
#reward_preds[task, step] = ptu.get_numpy(
# self.vae.reward_decoder(task_sample, next_obs, obs, action)[0, 0])
observations[task, step + 1, :] = ptu.get_numpy(
next_obs[0, :obs_size])
actions[task, step, :] = ptu.get_numpy(action[0, :])
log_probs[task, step] = ptu.get_numpy(log_prob[0])
if "is_goal_state" in dir(
self.env.unwrapped
) and self.env.unwrapped.is_goal_state():
success_rate[task] = 1.
# set: obs <- next_obs
obs = next_obs.clone()
step += 1
returns_per_episode[task, episode_idx] = running_reward
return returns_per_episode, success_rate, log_probs, observations, rewards, actions
def torch_ify(np_array_or_other):
if isinstance(np_array_or_other, np.ndarray):
return ptu.from_numpy(np_array_or_other)
else:
return np_array_or_other
def set_param_values_np(self, param_values):
torch_dict = OrderedDict()
for key, tensor in param_values.items():
torch_dict[key] = ptu.from_numpy(tensor)
self.load_state_dict(torch_dict)
