def step(self, policy_output):
assert policy_output.n_elems() == self.envs_running.size()[0]
outputs = policy_output.unfold()
alls = []
env_run = {}
for b in range(len(outputs)):
idx_env = self.envs_running[b]
action = policy_output["action"][b]
last_action = action
if (isinstance(self.gym_envs[0].action_space,
gym.spaces.Discrete)):
action = action.item()
last_action = last_action.unsqueeze(0)
else:
action = action.tolist()
last_action = last_action.unsqueeze(0)
initial_state = torch.tensor([False])
act = action
frame, reward, done, unused_info = self.gym_envs[idx_env].step(act)
reward = torch.tensor([reward])
frame = format_frame(frame)
if isinstance(frame, torch.Tensor):
frame = {"frame": frame}
if not done:
env_run[b] = idx_env
done = torch.tensor([done])
r = DictTensor({
"reward": reward,
"done": done,
"initial_state": initial_state,
"last_action": last_action,
**frame,
})
alls.append(r)
d = DictTensor.cat(alls)
keys = []
values = []
for key, value in env_run.items():
keys.append(key)
values.append(value)
dd = d.index(torch.tensor(keys).long())
old_envs_running = self.envs_running
self.envs_running = torch.tensor(values)
return (d, old_envs_running), (dd, self.envs_running)
def reset(self, env_info=DictTensor({})):
N = self.n_envs()
self.envs_running = torch.arange(N)
reward = torch.zeros(N)
last_action = None
if (isinstance(self.gym_envs[0].action_space, gym.spaces.Discrete)):
last_action = torch.zeros(N, dtype=torch.int64)
else:
a = self.gym_envs[0].action_space.sample()
a = torch.tensor(a).unsqueeze(0).repeat(N, 1)
last_action = a
done = torch.zeros(N).bool()
initial_state = torch.ones(N).bool()
frames = None
if (env_info.empty()):
frames = [format_frame(e.reset()) for e in self.gym_envs]
else:
frames = []
for n in range(len(self.gym_envs)):
v = {k: env_info[k][n].tolist() for k in env_info.keys()}
frames.append(format_frame(self.gym_envs[n].reset(env_info=v)))
_frames = []
for f in frames:
if isinstance(f, torch.Tensor):
_frames.append({"frame": f})
else:
_frames.append(f)
frames = [DictTensor(_f) for _f in _frames]
frames = DictTensor.cat(frames)
frames.set("reward", reward)
frames.set("done", done)
frames.set("initial_state", initial_state)
frames.set("last_action", last_action)
return frames, self.envs_running
def step(self, policy_output):
assert policy_output.n_elems() == self.n_envs()
outputs = policy_output.unfold()
alls = []
alls_after = []
env_run = {}
for b in range(len(outputs)):
action = policy_output["action"][b]
last_action = action
if (isinstance(self.gym_envs[0].action_space,
gym.spaces.Discrete)):
action = action.item()
last_action = last_action.unsqueeze(0)
else:
action = action.tolist()
last_action = last_action.unsqueeze(0)
initial_state = torch.tensor([False])
act = action
frame, reward, done, unused_info = self.gym_envs[b].step(act)
reward = torch.tensor([reward])
frame = format_frame(frame)
if isinstance(frame, torch.Tensor):
frame = {"frame": frame}
done = torch.tensor([done])
r = DictTensor({
"reward": reward,
"done": done,
"initial_state": initial_state,
"last_action": last_action,
**frame,
})
alls.append(r)
if done:
if "set" in dir(self.gym_envs[b]):
self.gym_envs[b].set(self.env_info[b])
if self.env_info.empty():
frame = self.gym_envs[b].reset()
else:
v = {k: env_info[k][b].tolist() for k in env_info.keys()}
frame = self.gym_envs[b].reset(env_info=v)
frame = format_frame(frame)
if isinstance(frame, torch.Tensor):
frame = {"frame": frame}
last_action = None
if (isinstance(self.gym_envs[0].action_space,
gym.spaces.Discrete)):
last_action = torch.zeros(1, dtype=torch.int64)
else:
a = self.gym_envs[0].action_space.sample()
a = torch.tensor([a])
last_action = a
initial_state = torch.tensor([True])
reward = torch.tensor([0.0])
r = DictTensor({
"reward": reward,
"done": done,
"initial_state": initial_state,
"last_action": last_action,
**frame,
})
alls_after.append(r)
else:
alls_after.append(r)
next_observation = DictTensor.cat(alls)
next_observation_next_slot = DictTensor.cat(alls_after)
return (
(next_observation, torch.arange(self.n_envs())),
(next_observation_next_slot, torch.arange(self.n_envs())),
)
