def next_state_reward_dist(self, state: State,
action: Action) -> StateDistribution:
probs = [self.noise_prob] * len(self.action_space)
probs[action.value] = 1 - self.epsilon
states = {}
for a in self.action_space:
sr = self._gridworld._next_state_reward(state, a)
if sr.state in states:
rp = states[sr.state]
states[sr.state] = RewardProbability(rp.reward + sr.reward,
rp.prob + probs[a.value])
else:
states[sr.state] = RewardProbability(sr.reward, probs[a.value])
return states
def next_state_reward_dist(self, state: State,
action: Action) -> StateDistribution:
probs = [self.noise_prob] * len(self.action_space)
assert isinstance(action.value,
int), f"got type {type(action.value)} instead of int"
# pyre-fixme[16]: `int` has no attribute `__setitem__`.
probs[action.value] = 1 - self.epsilon
states = {}
for a in self.action_space:
sr = self._gridworld._next_state_reward(state, a)
if sr.state in states:
rp = states[sr.state]
states[sr.state] = RewardProbability(
rp.reward + sr.reward,
# pyre-fixme[16]: `int` has no attribute `__getitem__`.
rp.prob + probs[a.value],
)
else:
states[sr.state] = RewardProbability(sr.reward, probs[a.value])
return states
def next_state_reward_dist(self, state: State,
action: Action) -> StateDistribution:
self._model.eval()
state_reward_tensor = (self._model(
torch.tensor(state.value, dtype=torch.float).reshape(
-1, self._model._state_dim).to(self._device),
torch.nn.functional.one_hot(
torch.tensor(action.value, dtype=torch.long),
self._model._action_dim,
).reshape(-1, self._model._action_dim).float().to(self._device),
).reshape(-1).cpu())
return {
State(state_reward_tensor[:self._model._state_dim]):
RewardProbability(state_reward_tensor[-1].item())
}
def next_state_reward_dist(self, state: State,
action: Action) -> StateDistribution:
sr = self._next_state_reward(state, action)
return {sr.state: RewardProbability(sr.reward, 1.0)}