def training_step(self, batch, batch_idx: int, optimizer_idx: int = 0):
assert (optimizer_idx == 0) or (self._num_optimizing_steps > 1)
if optimizer_idx == 0:
self.batches_processed_this_epoch += 1
self.all_batches_processed += 1
if self._training_step_generator is None:
if self._training_batch_type and isinstance(batch, dict):
batch = self._training_batch_type.from_dict(batch)
self._training_step_generator = self.train_step_gen(
batch, batch_idx)
ret = next(self._training_step_generator)
if optimizer_idx == self._num_optimizing_steps - 1:
if not self._verified_steps:
try:
next(self._training_step_generator)
except StopIteration:
self._verified_steps = True
if not self._verified_steps:
raise RuntimeError(
"training_step_gen() yields too many times."
"The number of yields should match the number of optimizers,"
f" in this case {self._num_optimizing_steps}")
self._training_step_generator = None
SummaryWriterContext.increase_global_step()
return ret
def test_global_step(self):
with TemporaryDirectory() as tmp_dir:
writer = SummaryWriter(tmp_dir)
writer.add_scalar = MagicMock()
with summary_writer_context(writer):
SummaryWriterContext.add_scalar("test", torch.ones(1))
SummaryWriterContext.increase_global_step()
SummaryWriterContext.add_scalar("test", torch.zeros(1))
writer.add_scalar.assert_has_calls([
call("test", torch.ones(1), global_step=0),
call("test", torch.zeros(1), global_step=1),
])
self.assertEqual(2, len(writer.add_scalar.mock_calls))
def run_episode(env: EnvWrapper,
agent: Agent,
mdp_id: int = 0,
max_steps: Optional[int] = None) -> Trajectory:
"""
Return sum of rewards from episode.
After max_steps (if specified), the environment is assumed to be terminal.
Can also specify the mdp_id and gamma of episode.
"""
trajectory = Trajectory()
obs = env.reset()
possible_actions_mask = env.possible_actions_mask
terminal = False
num_steps = 0
while not terminal:
action, log_prob = agent.act(obs, possible_actions_mask)
next_obs, reward, terminal, _ = env.step(action)
next_possible_actions_mask = env.possible_actions_mask
if max_steps is not None and num_steps >= (max_steps - 1):
terminal = True
# Only partially filled. Agent can fill in more fields.
transition = Transition(
mdp_id=mdp_id,
sequence_number=num_steps,
observation=obs,
action=action,
reward=float(reward),
terminal=bool(terminal),
log_prob=log_prob,
possible_actions_mask=possible_actions_mask,
)
agent.post_step(transition)
trajectory.add_transition(transition)
SummaryWriterContext.increase_global_step()
obs = next_obs
possible_actions_mask = next_possible_actions_mask
num_steps += 1
agent.post_episode(trajectory)
return trajectory