- for u in range(itl, result.size(1) - itl + 1, itl):
- print(f"{itl=} {u=} {result.size(1)=}")
- result[:, u - 1] = (-1) + 1 + escape.first_lookahead_rewards_code
- ar_mask = (t >= u).long() * (t < u + self.height * self.width).long()
- ar_mask = ar_mask[None, :]
- ar_mask = ar_mask.expand_as(result)
- result *= 1 - ar_mask
- ar()
- result[:, u - 1] = (1) + 1 + escape.first_lookahead_rewards_code
- ar_mask = (t >= self.height * self.width).long() * (
- t < self.height * self.width + 2
+ # Generate iteration after iteration
+
+ result = self.test_input[:250].clone()
+ # Erase all the content but that of the first iteration
+ result[:, self.it_len :] = -1
+ # Set the lookahead_reward of the firs to UNKNOWN
+ result[:, self.index_lookahead_reward] = greed.lookahead_reward2code(2)
+
+ t = torch.arange(result.size(1), device=result.device)[None, :]
+
+ for u in tqdm.tqdm(
+ range(0, result.size(1), self.it_len),
+ desc="thinking",
+ ):
+ # Generate the next state but keep the initial one, the
+ # lookahead_reward of previous iterations are set to
+ # UNKNOWN
+ if u > 0:
+ result[
+ :, u + self.index_lookahead_reward
+ ] = greed.lookahead_reward2code(2)
+ ar_mask = (t >= u + self.index_states).long() * (
+ t < u + self.index_states + self.state_len
+ ).long()
+ ar(result, ar_mask)
+
+ # Generate the action and reward with lookahead_reward to +1
+ result[:, u + self.index_lookahead_reward] = greed.lookahead_reward2code(1)
+ ar_mask = (t >= u + self.index_action).long() * (
+ t <= u + self.index_reward