fixed divide by zero error

Author: atgambardella

reinforcement_learning/actor_critic.py

@@ -69,7 +69,7 @@ def finish_episode():
  R = r + args.gamma * R
  rewards.insert(0, R)
  rewards = torch.Tensor(rewards)
- rewards = (rewards - rewards.mean()) / rewards.std()
+ rewards = (rewards - rewards.mean()) / (rewards.std() + np.finfo(np.float32).eps)
  for (action, value), r in zip(saved_actions, rewards):
  action.reinforce(r - value.data.squeeze())
  value_loss += F.smooth_l1_loss(value, Variable(torch.Tensor([r])))

reinforcement_learning/reinforce.py

@@ -65,7 +65,7 @@ def finish_episode():
  R = r + args.gamma * R
  rewards.insert(0, R)
  rewards = torch.Tensor(rewards)
- rewards = (rewards - rewards.mean()) / rewards.std()
+ rewards = (rewards - rewards.mean()) / (rewards.std() + np.finfo(np.float32).eps)
  for action, r in zip(model.saved_actions, rewards):
  action.reinforce(r)
  optimizer.zero_grad()