def get_value(self, state, belief, task, latent_sample, latent_mean,
latent_logvar):
latent = utl.get_latent_for_policy(self.args,
latent_sample=latent_sample,
latent_mean=latent_mean,
latent_logvar=latent_logvar)
return self.policy.actor_critic.get_value(state=state,
belief=belief,
task=task,
latent=latent).detach()
def before_update(self, policy):
latent = utl.get_latent_for_policy(
self.args,
latent_sample=torch.stack(self.latent_samples[:-1])
if self.latent_samples is not None else None,
latent_mean=torch.stack(self.latent_mean[:-1])
if self.latent_mean is not None else None,
latent_logvar=torch.stack(self.latent_logvar[:-1])
if self.latent_mean is not None else None)
_, action_log_probs, _ = policy.evaluate_actions(
self.prev_state[:-1], latent,
self.beliefs[:-1] if self.beliefs is not None else None,
self.tasks[:-1] if self.tasks is not None else None, self.actions)
self.action_log_probs = action_log_probs.detach()
def update_rms(self, args, policy_storage):
""" Update normalisation parameters for inputs with current data """
if self.pass_state_to_policy and self.norm_state:
state = policy_storage.prev_state[:-1]
self.state_rms.update(state)
if self.pass_latent_to_policy and self.norm_latent:
latent = utl.get_latent_for_policy(
args, torch.cat(policy_storage.latent_samples[:-1]),
torch.cat(policy_storage.latent_mean[:-1]),
torch.cat(policy_storage.latent_logvar[:-1]))
self.latent_rms.update(latent)
if self.pass_belief_to_policy and self.norm_belief:
self.belief_rms.update(policy_storage.beliefs[:-1])
if self.pass_task_to_policy and self.norm_task:
self.task_rms.update(policy_storage.tasks[:-1])
def update(
self,
policy_storage,
encoder=None, # variBAD encoder
rlloss_through_encoder=False, # whether or not to backprop RL loss through encoder
compute_vae_loss=None # function that can compute the VAE loss
):
# -- get action values --
advantages = policy_storage.returns[:
-1] - policy_storage.value_preds[:
-1]
advantages = (advantages - advantages.mean()) / (advantages.std() +
1e-5)
# if this is true, we will update the VAE at every PPO update
# otherwise, we update it after we update the policy
if rlloss_through_encoder:
# recompute embeddings (to build computation graph)
utl.recompute_embeddings(
policy_storage,
encoder,
sample=False,
update_idx=0,
detach_every=self.args.tbptt_stepsize if hasattr(
self.args, 'tbptt_stepsize') else None)
# update the normalisation parameters of policy inputs before updating
self.actor_critic.update_rms(args=self.args,
policy_storage=policy_storage)
# call this to make sure that the action_log_probs are computed
# (needs to be done right here because of some caching thing when normalising actions)
policy_storage.before_update(self.actor_critic)
value_loss_epoch = 0
action_loss_epoch = 0
dist_entropy_epoch = 0
loss_epoch = 0
for e in range(self.ppo_epoch):
data_generator = policy_storage.feed_forward_generator(
advantages, self.num_mini_batch)
for sample in data_generator:
state_batch, belief_batch, task_batch, \
actions_batch, latent_sample_batch, latent_mean_batch, latent_logvar_batch, value_preds_batch, \
return_batch, old_action_log_probs_batch, adv_targ = sample
if not rlloss_through_encoder:
state_batch = state_batch.detach()
if latent_sample_batch is not None:
latent_sample_batch = latent_sample_batch.detach()
latent_mean_batch = latent_mean_batch.detach()
latent_logvar_batch = latent_logvar_batch.detach()
latent_batch = utl.get_latent_for_policy(
args=self.args,
latent_sample=latent_sample_batch,
latent_mean=latent_mean_batch,
latent_logvar=latent_logvar_batch)
# Reshape to do in a single forward pass for all steps
values, action_log_probs, dist_entropy = \
self.actor_critic.evaluate_actions(state=state_batch, latent=latent_batch,
belief=belief_batch, task=task_batch,
action=actions_batch)
ratio = torch.exp(action_log_probs -
old_action_log_probs_batch)
surr1 = ratio * adv_targ
surr2 = torch.clamp(ratio, 1.0 - self.clip_param,
1.0 + self.clip_param) * adv_targ
action_loss = -torch.min(surr1, surr2).mean()
if self.use_huber_loss and self.use_clipped_value_loss:
value_pred_clipped = value_preds_batch + (
values - value_preds_batch).clamp(
-self.clip_param, self.clip_param)
value_losses = F.smooth_l1_loss(values,
return_batch,
reduction='none')
value_losses_clipped = F.smooth_l1_loss(value_pred_clipped,
return_batch,
reduction='none')
value_loss = 0.5 * torch.max(value_losses,
value_losses_clipped).mean()
elif self.use_huber_loss:
value_loss = F.smooth_l1_loss(values, return_batch)
elif self.use_clipped_value_loss:
value_pred_clipped = value_preds_batch + (
values - value_preds_batch).clamp(
-self.clip_param, self.clip_param)
value_losses = (values - return_batch).pow(2)
value_losses_clipped = (value_pred_clipped -
return_batch).pow(2)
value_loss = 0.5 * torch.max(value_losses,
value_losses_clipped).mean()
else:
value_loss = 0.5 * (return_batch - values).pow(2).mean()
# zero out the gradients
self.optimiser.zero_grad()
if rlloss_through_encoder:
self.optimiser_vae.zero_grad()
# compute policy loss and backprop
loss = value_loss * self.value_loss_coef + action_loss - dist_entropy * self.entropy_coef
# compute vae loss and backprop
if rlloss_through_encoder:
loss += self.args.vae_loss_coeff * compute_vae_loss()
# compute gradients (will attach to all networks involved in this computation)
loss.backward()
# clip gradients
nn.utils.clip_grad_norm_(self.actor_critic.parameters(),
self.args.policy_max_grad_norm)
if rlloss_through_encoder:
if self.args.encoder_max_grad_norm is not None:
nn.utils.clip_grad_norm_(
encoder.parameters(),
self.args.encoder_max_grad_norm)
# update
self.optimiser.step()
if rlloss_through_encoder:
self.optimiser_vae.step()
value_loss_epoch += value_loss.item()
action_loss_epoch += action_loss.item()
dist_entropy_epoch += dist_entropy.item()
loss_epoch += loss.item()
if rlloss_through_encoder:
# recompute embeddings (to build computation graph)
utl.recompute_embeddings(
policy_storage,
encoder,
sample=False,
update_idx=e + 1,
detach_every=self.args.tbptt_stepsize if hasattr(
self.args, 'tbptt_stepsize') else None)
if (not rlloss_through_encoder) and (self.optimiser_vae is not None):
for _ in range(self.args.num_vae_updates):
compute_vae_loss(update=True)
if self.lr_scheduler_policy is not None:
self.lr_scheduler_policy.step()
if self.lr_scheduler_encoder is not None:
self.lr_scheduler_encoder.step()
num_updates = self.ppo_epoch * self.num_mini_batch
value_loss_epoch /= num_updates
action_loss_epoch /= num_updates
dist_entropy_epoch /= num_updates
loss_epoch /= num_updates
return value_loss_epoch, action_loss_epoch, dist_entropy_epoch, loss_epoch
def visualise_behaviour(
env,
args,
policy,
iter_idx,
encoder=None,
image_folder=None,
return_pos=False,
**kwargs,
):
num_episodes = args.max_rollouts_per_task
unwrapped_env = env.venv.unwrapped.envs[0].unwrapped
# --- initialise things we want to keep track of ---
episode_prev_obs = [[] for _ in range(num_episodes)]
episode_next_obs = [[] for _ in range(num_episodes)]
episode_actions = [[] for _ in range(num_episodes)]
episode_rewards = [[] for _ in range(num_episodes)]
episode_returns = []
episode_lengths = []
if encoder is not None:
episode_latent_samples = [[] for _ in range(num_episodes)]
episode_latent_means = [[] for _ in range(num_episodes)]
episode_latent_logvars = [[] for _ in range(num_episodes)]
else:
curr_latent_sample = curr_latent_mean = curr_latent_logvar = None
episode_latent_samples = episode_latent_means = episode_latent_logvars = None
# --- roll out policy ---
# (re)set environment
env.reset_task()
state, belief, task = utl.reset_env(env, args)
start_state = state.clone()
# if hasattr(args, 'hidden_size'):
# hidden_state = torch.zeros((1, args.hidden_size)).to(device)
# else:
# hidden_state = None
# keep track of what task we're in and the position of the cheetah
pos = [[] for _ in range(args.max_rollouts_per_task)]
start_pos = unwrapped_env.get_body_com("torso")[0].copy()
for episode_idx in range(num_episodes):
curr_rollout_rew = []
pos[episode_idx].append(start_pos)
if encoder is not None:
if episode_idx == 0:
# reset to prior
curr_latent_sample, curr_latent_mean, curr_latent_logvar, hidden_state = encoder.prior(
1)
curr_latent_sample = curr_latent_sample[0].to(device)
curr_latent_mean = curr_latent_mean[0].to(device)
curr_latent_logvar = curr_latent_logvar[0].to(device)
episode_latent_samples[episode_idx].append(
curr_latent_sample[0].clone())
episode_latent_means[episode_idx].append(
curr_latent_mean[0].clone())
episode_latent_logvars[episode_idx].append(
curr_latent_logvar[0].clone())
for step_idx in range(1, env._max_episode_steps + 1):
if step_idx == 1:
episode_prev_obs[episode_idx].append(start_state.clone())
else:
episode_prev_obs[episode_idx].append(state.clone())
# act
latent = utl.get_latent_for_policy(
args,
latent_sample=curr_latent_sample,
latent_mean=curr_latent_mean,
latent_logvar=curr_latent_logvar)
_, action = policy.act(state=state.view(-1),
latent=latent,
belief=belief,
task=task,
deterministic=True)
(state, belief,
task), (rew, rew_normalised), done, info = utl.env_step(
env, action, args)
state = state.reshape((1, -1)).float().to(device)
# keep track of position
pos[episode_idx].append(
unwrapped_env.get_body_com("torso")[0].copy())
if encoder is not None:
# update task embedding
curr_latent_sample, curr_latent_mean, curr_latent_logvar, hidden_state = encoder(
action.reshape(1, -1).float().to(device),
state,
rew.reshape(1, -1).float().to(device),
hidden_state,
return_prior=False)
episode_latent_samples[episode_idx].append(
curr_latent_sample[0].clone())
episode_latent_means[episode_idx].append(
curr_latent_mean[0].clone())
episode_latent_logvars[episode_idx].append(
curr_latent_logvar[0].clone())
episode_next_obs[episode_idx].append(state.clone())
episode_rewards[episode_idx].append(rew.clone())
episode_actions[episode_idx].append(
action.reshape(1, -1).clone())
if info[0]['done_mdp'] and not done:
start_state = info[0]['start_state']
start_state = torch.from_numpy(start_state).reshape(
(1, -1)).float().to(device)
start_pos = unwrapped_env.get_body_com("torso")[0].copy()
break
episode_returns.append(sum(curr_rollout_rew))
episode_lengths.append(step_idx)
# clean up
if encoder is not None:
episode_latent_means = [
torch.stack(e) for e in episode_latent_means
]
episode_latent_logvars = [
torch.stack(e) for e in episode_latent_logvars
]
episode_prev_obs = [torch.cat(e) for e in episode_prev_obs]
episode_next_obs = [torch.cat(e) for e in episode_next_obs]
episode_actions = [torch.cat(e) for e in episode_actions]
episode_rewards = [torch.cat(e) for e in episode_rewards]
# plot the movement of the half-cheetah
plt.figure(figsize=(7, 4 * num_episodes))
min_x = min([min(p) for p in pos])
max_x = max([max(p) for p in pos])
span = max_x - min_x
for i in range(num_episodes):
plt.subplot(num_episodes, 1, i + 1)
# (not plotting the last step because this gives weird artefacts)
plt.plot(pos[i][:-1], range(len(pos[i][:-1])), 'k')
plt.title('task: {}'.format(task), fontsize=15)
plt.ylabel('steps (ep {})'.format(i), fontsize=15)
if i == num_episodes - 1:
plt.xlabel('position', fontsize=15)
# else:
# plt.xticks([])
plt.xlim(min_x - 0.05 * span, max_x + 0.05 * span)
plt.plot([0, 0], [200, 200], 'b--', alpha=0.2)
plt.tight_layout()
if image_folder is not None:
plt.savefig('{}/{}_behaviour'.format(image_folder, iter_idx))
plt.close()
else:
plt.show()
if not return_pos:
return episode_latent_means, episode_latent_logvars, \
episode_prev_obs, episode_next_obs, episode_actions, episode_rewards, \
episode_returns
else:
return episode_latent_means, episode_latent_logvars, \
episode_prev_obs, episode_next_obs, episode_actions, episode_rewards, \
episode_returns, pos
def update(
self,
policy_storage,
encoder=None, # variBAD encoder
rlloss_through_encoder=False, # whether or not to backprop RL loss through encoder
compute_vae_loss=None # function that can compute the VAE loss
):
# get action values
advantages = policy_storage.returns[:
-1] - policy_storage.value_preds[:
-1]
if rlloss_through_encoder:
# re-compute encoding (to build the computation graph from scratch)
utl.recompute_embeddings(
policy_storage,
encoder,
sample=False,
update_idx=0,
detach_every=self.args.tbptt_stepsize if hasattr(
self.args, 'tbptt_stepsize') else None)
# update the normalisation parameters of policy inputs before updating
self.actor_critic.update_rms(args=self.args,
policy_storage=policy_storage)
data_generator = policy_storage.feed_forward_generator(advantages, 1)
for sample in data_generator:
state_batch, belief_batch, task_batch, \
actions_batch, latent_sample_batch, latent_mean_batch, latent_logvar_batch, value_preds_batch, \
return_batch, old_action_log_probs_batch, adv_targ = sample
if not rlloss_through_encoder:
state_batch = state_batch.detach()
if latent_sample_batch is not None:
latent_sample_batch = latent_sample_batch.detach()
latent_mean_batch = latent_mean_batch.detach()
latent_logvar_batch = latent_logvar_batch.detach()
latent_batch = utl.get_latent_for_policy(
args=self.args,
latent_sample=latent_sample_batch,
latent_mean=latent_mean_batch,
latent_logvar=latent_logvar_batch)
values, action_log_probs, dist_entropy, action_mean, action_logstd = \
self.actor_critic.evaluate_actions(state=state_batch, latent=latent_batch,
belief=belief_batch, task=task_batch,
action=actions_batch, return_action_mean=True)
# -- UPDATE --
# zero out the gradients
self.optimiser.zero_grad()
if rlloss_through_encoder:
self.optimiser_vae.zero_grad()
# compute policy loss and backprop
value_loss = (return_batch - values).pow(2).mean()
action_loss = -(adv_targ.detach() * action_log_probs).mean()
# (loss = value loss + action loss + entropy loss, weighted)
loss = value_loss * self.value_loss_coef + action_loss - dist_entropy * self.entropy_coef
# compute vae loss and backprop
if rlloss_through_encoder:
loss += self.args.vae_loss_coeff * compute_vae_loss()
# compute gradients (will attach to all networks involved in this computation)
loss.backward()
nn.utils.clip_grad_norm_(self.actor_critic.parameters(),
self.args.policy_max_grad_norm)
if encoder is not None and rlloss_through_encoder:
nn.utils.clip_grad_norm_(encoder.parameters(),
self.args.policy_max_grad_norm)
# update
self.optimiser.step()
if rlloss_through_encoder:
self.optimiser_vae.step()
if (not rlloss_through_encoder) and (self.optimiser_vae is not None):
for _ in range(self.args.num_vae_updates):
compute_vae_loss(update=True)
if self.lr_scheduler_policy is not None:
self.lr_scheduler_policy.step()
if self.lr_scheduler_encoder is not None:
self.lr_scheduler_encoder.step()
return value_loss, action_loss, dist_entropy, loss
def visualise_behaviour(
self,
env,
args,
policy,
iter_idx,
encoder=None,
image_folder=None,
return_pos=False,
**kwargs,
):
num_episodes = args.max_rollouts_per_task
# --- initialise things we want to keep track of ---
episode_prev_obs = [[] for _ in range(num_episodes)]
episode_next_obs = [[] for _ in range(num_episodes)]
episode_actions = [[] for _ in range(num_episodes)]
episode_rewards = [[] for _ in range(num_episodes)]
episode_returns = []
episode_lengths = []
if encoder is not None:
episode_latent_samples = [[] for _ in range(num_episodes)]
episode_latent_means = [[] for _ in range(num_episodes)]
episode_latent_logvars = [[] for _ in range(num_episodes)]
else:
episode_latent_samples = episode_latent_means = episode_latent_logvars = None
# --- roll out policy ---
# (re)set environment
env.reset_task()
state, belief, task = utl.reset_env(env, args)
start_obs_raw = state.clone()
task = task.view(-1) if task is not None else None
# initialise actions and rewards (used as initial input to policy if we have a recurrent policy)
if hasattr(args, 'hidden_size'):
hidden_state = torch.zeros((1, args.hidden_size)).to(device)
else:
hidden_state = None
# keep track of what task we're in and the position of the cheetah
pos = [[] for _ in range(args.max_rollouts_per_task)]
start_pos = state
for episode_idx in range(num_episodes):
curr_rollout_rew = []
pos[episode_idx].append(start_pos[0])
if episode_idx == 0:
if encoder is not None:
# reset to prior
curr_latent_sample, curr_latent_mean, curr_latent_logvar, hidden_state = encoder.prior(
1)
curr_latent_sample = curr_latent_sample[0].to(device)
curr_latent_mean = curr_latent_mean[0].to(device)
curr_latent_logvar = curr_latent_logvar[0].to(device)
else:
curr_latent_sample = curr_latent_mean = curr_latent_logvar = None
if encoder is not None:
episode_latent_samples[episode_idx].append(
curr_latent_sample[0].clone())
episode_latent_means[episode_idx].append(
curr_latent_mean[0].clone())
episode_latent_logvars[episode_idx].append(
curr_latent_logvar[0].clone())
for step_idx in range(1, env._max_episode_steps + 1):
if step_idx == 1:
episode_prev_obs[episode_idx].append(start_obs_raw.clone())
else:
episode_prev_obs[episode_idx].append(state.clone())
# act
latent = utl.get_latent_for_policy(
args,
latent_sample=curr_latent_sample,
latent_mean=curr_latent_mean,
latent_logvar=curr_latent_logvar)
_, action = policy.act(state=state.view(-1),
latent=latent,
belief=belief,
task=task,
deterministic=True)
(state, belief,
task), (rew, rew_normalised), done, info = utl.env_step(
env, action, args)
state = state.float().reshape((1, -1)).to(device)
task = task.view(-1) if task is not None else None
# keep track of position
pos[episode_idx].append(state[0])
if encoder is not None:
# update task embedding
curr_latent_sample, curr_latent_mean, curr_latent_logvar, hidden_state = encoder(
action.reshape(1, -1).float().to(device),
state,
rew.reshape(1, -1).float().to(device),
hidden_state,
return_prior=False)
episode_latent_samples[episode_idx].append(
curr_latent_sample[0].clone())
episode_latent_means[episode_idx].append(
curr_latent_mean[0].clone())
episode_latent_logvars[episode_idx].append(
curr_latent_logvar[0].clone())
episode_next_obs[episode_idx].append(state.clone())
episode_rewards[episode_idx].append(rew.clone())
episode_actions[episode_idx].append(action.clone())
if info[0]['done_mdp'] and not done:
start_obs_raw = info[0]['start_state']
start_obs_raw = torch.from_numpy(
start_obs_raw).float().reshape((1, -1)).to(device)
start_pos = start_obs_raw
break
episode_returns.append(sum(curr_rollout_rew))
episode_lengths.append(step_idx)
# clean up
if encoder is not None:
episode_latent_means = [
torch.stack(e) for e in episode_latent_means
]
episode_latent_logvars = [
torch.stack(e) for e in episode_latent_logvars
]
episode_prev_obs = [torch.cat(e) for e in episode_prev_obs]
episode_next_obs = [torch.cat(e) for e in episode_next_obs]
episode_actions = [torch.stack(e) for e in episode_actions]
episode_rewards = [torch.cat(e) for e in episode_rewards]
figsize = (5.5, 4)
figure, axis = plt.subplots(1, 1, figsize=figsize)
xlim = (-1.3, 1.3)
if self.goal_sampler == semi_circle_goal_sampler:
ylim = (-0.3, 1.3)
else:
ylim = (-1.3, 1.3)
color_map = mpl.colors.ListedColormap(
sns.color_palette("husl", num_episodes))
observations = torch.stack(
[episode_prev_obs[i] for i in range(num_episodes)]).cpu().numpy()
curr_task = env.get_task()
# plot goal
axis.scatter(*curr_task, marker='x', color='k', s=50)
# radius where we get reward
if hasattr(self, 'goal_radius'):
circle1 = plt.Circle(curr_task,
self.goal_radius,
color='c',
alpha=0.2,
edgecolor='none')
plt.gca().add_artist(circle1)
for i in range(num_episodes):
color = color_map(i)
path = observations[i]
# plot (semi-)circle
r = 1.0
if self.goal_sampler == semi_circle_goal_sampler:
angle = np.linspace(0, np.pi, 100)
else:
angle = np.linspace(0, 2 * np.pi, 100)
goal_range = r * np.array((np.cos(angle), np.sin(angle)))
plt.plot(goal_range[0], goal_range[1], 'k--', alpha=0.1)
# plot trajectory
axis.plot(path[:, 0], path[:, 1], '-', color=color, label=i)
axis.scatter(*path[0, :2], marker='.', color=color, s=50)
plt.xlim(xlim)
plt.ylim(ylim)
plt.xticks([])
plt.yticks([])
plt.legend()
plt.tight_layout()
if image_folder is not None:
plt.savefig('{}/{}_behaviour.png'.format(image_folder, iter_idx),
dpi=300,
bbox_inches='tight')
plt.close()
else:
plt.show()
plt_rew = [
episode_rewards[i][:episode_lengths[i]]
for i in range(len(episode_rewards))
]
plt.plot(torch.cat(plt_rew).view(-1).cpu().numpy())
plt.xlabel('env step')
plt.ylabel('reward per step')
plt.tight_layout()
if image_folder is not None:
plt.savefig('{}/{}_rewards.png'.format(image_folder, iter_idx),
dpi=300,
bbox_inches='tight')
plt.close()
else:
plt.show()
if not return_pos:
return episode_latent_means, episode_latent_logvars, \
episode_prev_obs, episode_next_obs, episode_actions, episode_rewards, \
episode_returns
else:
return episode_latent_means, episode_latent_logvars, \
episode_prev_obs, episode_next_obs, episode_actions, episode_rewards, \
episode_returns, pos
def get_test_rollout(args, env, policy, encoder=None):
num_episodes = args.max_rollouts_per_task
# --- initialise things we want to keep track of ---
episode_prev_obs = [[] for _ in range(num_episodes)]
episode_next_obs = [[] for _ in range(num_episodes)]
episode_actions = [[] for _ in range(num_episodes)]
episode_rewards = [[] for _ in range(num_episodes)]
episode_returns = []
episode_lengths = []
if encoder is not None:
episode_latent_samples = [[] for _ in range(num_episodes)]
episode_latent_means = [[] for _ in range(num_episodes)]
episode_latent_logvars = [[] for _ in range(num_episodes)]
else:
curr_latent_sample = curr_latent_mean = curr_latent_logvar = None
episode_latent_means = episode_latent_logvars = None
# --- roll out policy ---
# (re)set environment
env.reset_task()
state, belief, task = utl.reset_env(env, args)
state = state.reshape((1, -1)).to(device)
task = task.view(-1) if task is not None else None
for episode_idx in range(num_episodes):
curr_rollout_rew = []
if encoder is not None:
if episode_idx == 0:
# reset to prior
curr_latent_sample, curr_latent_mean, curr_latent_logvar, hidden_state = encoder.prior(
1)
curr_latent_sample = curr_latent_sample[0].to(device)
curr_latent_mean = curr_latent_mean[0].to(device)
curr_latent_logvar = curr_latent_logvar[0].to(device)
episode_latent_samples[episode_idx].append(
curr_latent_sample[0].clone())
episode_latent_means[episode_idx].append(
curr_latent_mean[0].clone())
episode_latent_logvars[episode_idx].append(
curr_latent_logvar[0].clone())
for step_idx in range(1, env._max_episode_steps + 1):
episode_prev_obs[episode_idx].append(state.clone())
latent = utl.get_latent_for_policy(
args,
latent_sample=curr_latent_sample,
latent_mean=curr_latent_mean,
latent_logvar=curr_latent_logvar)
_, action = policy.act(state=state.view(-1),
latent=latent,
belief=belief,
task=task,
deterministic=True)
action = action.reshape((1, *action.shape))
# observe reward and next obs
(state, belief,
task), (rew_raw, rew_normalised), done, infos = utl.env_step(
env, action, args)
state = state.reshape((1, -1)).to(device)
task = task.view(-1) if task is not None else None
if encoder is not None:
# update task embedding
curr_latent_sample, curr_latent_mean, curr_latent_logvar, hidden_state = encoder(
action.float().to(device),
state,
rew_raw.reshape((1, 1)).float().to(device),
hidden_state,
return_prior=False)
episode_latent_samples[episode_idx].append(
curr_latent_sample[0].clone())
episode_latent_means[episode_idx].append(
curr_latent_mean[0].clone())
episode_latent_logvars[episode_idx].append(
curr_latent_logvar[0].clone())
episode_next_obs[episode_idx].append(state.clone())
episode_rewards[episode_idx].append(rew_raw.clone())
episode_actions[episode_idx].append(action.clone())
if infos[0]['done_mdp']:
break
episode_returns.append(sum(curr_rollout_rew))
episode_lengths.append(step_idx)
# clean up
if encoder is not None:
episode_latent_means = [torch.stack(e) for e in episode_latent_means]
episode_latent_logvars = [
torch.stack(e) for e in episode_latent_logvars
]
episode_prev_obs = [torch.cat(e) for e in episode_prev_obs]
episode_next_obs = [torch.cat(e) for e in episode_next_obs]
episode_actions = [torch.cat(e) for e in episode_actions]
episode_rewards = [torch.cat(r) for r in episode_rewards]
return episode_latent_means, episode_latent_logvars, \
episode_prev_obs, episode_next_obs, episode_actions, episode_rewards, \
episode_returns
def visualise_behaviour(
env,
args,
policy,
iter_idx,
encoder=None,
image_folder=None,
return_pos=False,
**kwargs,
):
num_episodes = args.max_rollouts_per_task
unwrapped_env = env.venv.unwrapped.envs[0].unwrapped
# --- initialise things we want to keep track of ---
episode_prev_obs = [[] for _ in range(num_episodes)]
episode_next_obs = [[] for _ in range(num_episodes)]
episode_actions = [[] for _ in range(num_episodes)]
episode_rewards = [[] for _ in range(num_episodes)]
episode_returns = []
episode_lengths = []
if encoder is not None:
episode_latent_samples = [[] for _ in range(num_episodes)]
episode_latent_means = [[] for _ in range(num_episodes)]
episode_latent_logvars = [[] for _ in range(num_episodes)]
else:
episode_latent_samples = episode_latent_means = episode_latent_logvars = None
# --- roll out policy ---
# (re)set environment
env.reset_task()
state, belief, task = utl.reset_env(env, args)
start_obs_raw = state.clone()
task = task.view(-1) if task is not None else None
# initialise actions and rewards (used as initial input to policy if we have a recurrent policy)
if hasattr(args, 'hidden_size'):
hidden_state = torch.zeros((1, args.hidden_size)).to(device)
else:
hidden_state = None
# keep track of what task we're in and the position of the cheetah
pos = [[] for _ in range(args.max_rollouts_per_task)]
start_pos = unwrapped_env.get_body_com("torso")[:2].copy()
for episode_idx in range(num_episodes):
curr_rollout_rew = []
pos[episode_idx].append(start_pos)
if episode_idx == 0:
if encoder is not None:
# reset to prior
curr_latent_sample, curr_latent_mean, curr_latent_logvar, hidden_state = encoder.prior(
1)
curr_latent_sample = curr_latent_sample[0].to(device)
curr_latent_mean = curr_latent_mean[0].to(device)
curr_latent_logvar = curr_latent_logvar[0].to(device)
else:
curr_latent_sample = curr_latent_mean = curr_latent_logvar = None
if encoder is not None:
episode_latent_samples[episode_idx].append(
curr_latent_sample[0].clone())
episode_latent_means[episode_idx].append(
curr_latent_mean[0].clone())
episode_latent_logvars[episode_idx].append(
curr_latent_logvar[0].clone())
for step_idx in range(1, env._max_episode_steps + 1):
if step_idx == 1:
episode_prev_obs[episode_idx].append(start_obs_raw.clone())
else:
episode_prev_obs[episode_idx].append(state.clone())
# act
latent = utl.get_latent_for_policy(
args,
latent_sample=curr_latent_sample,
latent_mean=curr_latent_mean,
latent_logvar=curr_latent_logvar)
_, action, _ = policy.act(state=state.view(-1),
latent=latent,
belief=belief,
task=task,
deterministic=True)
(state, belief,
task), (rew, rew_normalised), done, info = utl.env_step(
env, action, args)
state = state.float().reshape((1, -1)).to(device)
task = task.view(-1) if task is not None else None
# keep track of position
pos[episode_idx].append(
unwrapped_env.get_body_com("torso")[:2].copy())
if encoder is not None:
# update task embedding
curr_latent_sample, curr_latent_mean, curr_latent_logvar, hidden_state = encoder(
action.reshape(1, -1).float().to(device),
state,
rew.reshape(1, -1).float().to(device),
hidden_state,
return_prior=False)
episode_latent_samples[episode_idx].append(
curr_latent_sample[0].clone())
episode_latent_means[episode_idx].append(
curr_latent_mean[0].clone())
episode_latent_logvars[episode_idx].append(
curr_latent_logvar[0].clone())
episode_next_obs[episode_idx].append(state.clone())
episode_rewards[episode_idx].append(rew.clone())
episode_actions[episode_idx].append(action.clone())
if info[0]['done_mdp'] and not done:
start_obs_raw = info[0]['start_state']
start_obs_raw = torch.from_numpy(
start_obs_raw).float().reshape((1, -1)).to(device)
start_pos = unwrapped_env.get_body_com("torso")[:2].copy()
break
episode_returns.append(sum(curr_rollout_rew))
episode_lengths.append(step_idx)
# clean up
if encoder is not None:
episode_latent_means = [
torch.stack(e) for e in episode_latent_means
]
episode_latent_logvars = [
torch.stack(e) for e in episode_latent_logvars
]
episode_prev_obs = [torch.cat(e) for e in episode_prev_obs]
episode_next_obs = [torch.cat(e) for e in episode_next_obs]
episode_actions = [torch.stack(e) for e in episode_actions]
episode_rewards = [torch.cat(e) for e in episode_rewards]
# plot the movement of the ant
# print(pos)
plt.figure(figsize=(5, 4 * num_episodes))
min_dim = -3.5
max_dim = 3.5
span = max_dim - min_dim
for i in range(num_episodes):
plt.subplot(num_episodes, 1, i + 1)
x = list(map(lambda p: p[0], pos[i]))
y = list(map(lambda p: p[1], pos[i]))
plt.plot(x[0], y[0], 'bo')
plt.scatter(x, y, 1, 'g')
curr_task = env.get_task()
plt.title('task: {}'.format(curr_task), fontsize=15)
if 'Goal' in args.env_name:
plt.plot(curr_task[0], curr_task[1], 'rx')
plt.ylabel('y-position (ep {})'.format(i), fontsize=15)
if i == num_episodes - 1:
plt.xlabel('x-position', fontsize=15)
plt.ylabel('y-position (ep {})'.format(i), fontsize=15)
plt.xlim(min_dim - 0.05 * span, max_dim + 0.05 * span)
plt.ylim(min_dim - 0.05 * span, max_dim + 0.05 * span)
plt.tight_layout()
if image_folder is not None:
plt.savefig('{}/{}_behaviour'.format(image_folder, iter_idx))
plt.close()
else:
plt.show()
if not return_pos:
return episode_latent_means, episode_latent_logvars, \
episode_prev_obs, episode_next_obs, episode_actions, episode_rewards, \
episode_returns
else:
return episode_latent_means, episode_latent_logvars, \
episode_prev_obs, episode_next_obs, episode_actions, episode_rewards, \
episode_returns, pos