def step(
self,
vector_action: Dict[str, Any] = None,
memory: Dict[str, Any] = None,
text_action: Dict[str, Any] = None,
value: Dict[str, Any] = None,
) -> AllBrainInfo:
assert vector_action is not None
delta = vector_action[BRAIN_NAME][0][0]
delta = clamp(delta, -STEP_SIZE, STEP_SIZE)
self.position += delta
self.position = clamp(self.position, -1, 1)
self.step_count += 1
done = self.position >= 1.0 or self.position <= -1.0
if done:
reward = SUCCESS_REWARD * self.position
else:
reward = -TIME_PENALTY
agent_info = AgentInfoProto(
stacked_vector_observation=[self.position] * OBS_SIZE,
reward=reward,
done=done,
)
if done:
self._reset_agent()
return {
BRAIN_NAME:
BrainInfo.from_agent_proto(0, [agent_info],
self._brains[BRAIN_NAME])
}
def reset(
self,
config: Dict[str, float] = None,
train_mode: bool = True,
custom_reset_parameters: Any = None,
) -> AllBrainInfo: # type: ignore
self._reset_agent()
agent_info = AgentInfoProto(
stacked_vector_observation=[self.position] * OBS_SIZE,
done=False,
max_step_reached=False,
)
return {
BRAIN_NAME:
BrainInfo.from_agent_proto(0, [agent_info],
self._brains[BRAIN_NAME])
}