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Speed-up to O(1) from O(N) of the computation of each return in REINFORCE #1083

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Merged
merged 1 commit into from
Oct 17, 2022
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Speed-up to O(1) from O(N) of the computation of each return in REINFORCE #1083

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6 changes: 3 additions & 3 deletions reinforcement_learning/reinforce.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
import gym
import numpy as np
from itertools import count

from collections import deque
import torch
import torch.nn as nn
import torch.nn.functional as F
Expand Down Expand Up @@ -62,10 +62,10 @@ def select_action(state):
def finish_episode():
R = 0
policy_loss = []
returns = []
returns = deque()
for r in policy.rewards[::-1]:
R = r + args.gamma * R
returns.insert(0, R)
returns.appendleft(R)
returns = torch.tensor(returns)
returns = (returns - returns.mean()) / (returns.std() + eps)
for log_prob, R in zip(policy.saved_log_probs, returns):
Expand Down