def __call__(self,
time_steps,
sample=True,
evaluation=False,
return_on_done=False,
render=False,
render_image=False,
sleep_time=0,
reset_first=False,
reset_kwargs=None,
action_kwargs=None,
random_action=False,
get_last_val=False):
traj = Trajectory()
if reset_kwargs is None:
reset_kwargs = {}
if action_kwargs is None:
action_kwargs = {}
if evaluation:
env = self.eval_env
else:
env = self.train_env
if self.obs is None or reset_first or evaluation:
self.reset(env=env, **reset_kwargs)
ob = self.obs
# this is critical for some environments depending
# on the returned ob data. use deepcopy() to avoid
# adding the same ob to the traj
# only add deepcopy() when a new ob is generated
# so that traj[t].next_ob is still the same instance as traj[t+1].ob
ob = deepcopy(ob)
if return_on_done:
all_dones = np.zeros(env.num_envs, dtype=bool)
else:
all_dones = None
for t in range(time_steps):
if render:
env.render()
if sleep_time > 0:
time.sleep(sleep_time)
if render_image:
# get render images at the same time step as ob
imgs = get_render_images(env)
if random_action:
action = env.random_actions()
action_info = dict()
else:
action, action_info = self.agent.get_action(ob,
sample=sample,
**action_kwargs)
next_ob, reward, done, info = env.step(action)
if render_image:
for img, inf in zip(imgs, info):
inf['render_image'] = deepcopy(img)
true_next_ob, true_done, all_dones = self.get_true_done_next_ob(
next_ob, done, reward, info, all_dones, skip_record=evaluation)
sd = StepData(ob=ob,
action=action,
action_info=action_info,
next_ob=true_next_ob,
reward=reward,
done=true_done,
info=info)
ob = next_ob
traj.add(sd)
if return_on_done and np.all(all_dones):
break
if get_last_val and not evaluation:
last_val = self.agent.get_val(traj[-1].next_ob)
traj.add_extra('last_val', torch_to_np(last_val))
self.obs = ob if not evaluation else None
return traj
def __call__(self,
time_steps,
sample=True,
evaluation=False,
return_on_done=False,
render=False,
render_image=False,
sleep_time=0,
reset_first=False,
reset_kwargs=None,
action_kwargs=None,
get_last_val=False):
traj = Trajectory()
if reset_kwargs is None:
reset_kwargs = {}
if action_kwargs is None:
action_kwargs = {}
if evaluation:
env = self.eval_env
else:
env = self.train_env
# In RL^2, we should always reset in the begining of a rollout
if self.obs is None or reset_first or evaluation:
self.reset(**reset_kwargs)
ob = self.obs
hidden_state = self.hidden_states
# this is critical for some environments depending
# on the returned ob data. use deepcopy() to avoid
# adding the same ob to the traj
# only add deepcopy() when a new ob is generated
# so that traj[t].next_ob is still the same instance as traj[t+1].ob
ob = deepcopy(ob)
if return_on_done:
all_dones = np.zeros(env.num_envs, dtype=bool)
else:
all_dones = None
done = None
for t in range(time_steps):
if render:
env.render()
if sleep_time > 0:
time.sleep(sleep_time)
if render_image:
# get render images at the same time step as ob
imgs = deepcopy(env.get_images())
action, action_info, hidden_state = self.agent.get_action(
ob, sample=sample, hidden_state=hidden_state, **action_kwargs)
#print('action_info', action_info)
if self.hidden_state_shape is None:
self.hidden_state_shape = hidden_state.shape
next_ob, reward, done, info = env.step(action)
if render_image:
for img, inf in zip(imgs, info):
inf['render_image'] = deepcopy(img)
true_next_ob, true_done, all_dones = self.get_true_done_next_ob(
next_ob, done, reward, info, all_dones)
sd = StepData(
ob=ob,
action=action,
action_info=action_info,
next_ob=true_next_ob,
reward=reward,
done=true_done,
info=info,
extra=
done, # this is a flag that can tell whether the environment
# is reset or not so that we know whether we need to
# reset the hidden state or not. We save it in "extra"
)
ob = next_ob
traj.add(sd)
if return_on_done and np.all(all_dones):
break
# the order of next few lines matter, do not exchange
if get_last_val and not evaluation and t == time_steps - 1:
last_val, _ = self.agent.get_val(traj[-1].next_ob_raw,
hidden_state=hidden_state)
if last_val is not None:
traj.add_extra('last_val', torch_to_np(last_val))
else:
traj.add_extra('last_val', None)
hidden_state = self.check_hidden_state(hidden_state, done=done)
self.obs = ob if not evaluation else None
self.hidden_states = hidden_state.detach() if not evaluation else None
return traj
def __call__(self,
time_steps,
sample=True,
evaluation=False,
return_on_done=False,
render=False,
render_image=False,
sleep_time=0,
reset_kwargs=None,
action_kwargs=None):
traj = Trajectory()
if reset_kwargs is None:
reset_kwargs = {}
if action_kwargs is None:
action_kwargs = {}
if evaluation:
env = self.eval_env
else:
env = self.train_env
ob = env.reset(**reset_kwargs)
# this is critical for some environments depending
# on the returned ob data. use deepcopy() to avoid
# adding the same ob to the traj
# only add deepcopy() when a new ob is generated
# so that traj[t].next_ob is still the same instance as traj[t+1].ob
ob = deepcopy(ob)
if return_on_done:
all_dones = np.zeros(env.num_envs, dtype=bool)
for t in range(time_steps):
if render:
env.render()
if sleep_time > 0:
time.sleep(sleep_time)
if render_image:
# get render images at the same time step as ob
imgs = deepcopy(env.get_images())
action, action_info = self.agent.get_action(ob,
sample=sample,
**action_kwargs)
next_ob, reward, done, info = env.step(action)
next_ob = deepcopy(next_ob)
if render_image:
for img, inf in zip(imgs, info):
inf['render_image'] = deepcopy(img)
done_idx = np.argwhere(done).flatten()
if done_idx.size > 0 and return_on_done:
# vec env automatically resets the environment when it's done
# so the returned next_ob is not actually the next observation
all_dones[done_idx] = True
sd = StepData(ob=ob,
action=deepcopy(action),
action_info=deepcopy(action_info),
next_ob=next_ob,
reward=deepcopy(reward),
done=deepcopy(done),
info=deepcopy(info))
ob = next_ob
traj.add(sd)
if return_on_done and np.all(all_dones):
break
if not evaluation:
#print("next_ob:", traj[-1].next_ob)
last_val = self.agent.get_val(traj[-1].next_ob_raw)
traj.add_extra('last_val', torch_to_np(last_val))
return traj