wassname/pytorch-a2c-ppo-acktr
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Ilya Kostrikov
2017-09-09 10:09:03 -04:00
parent 9016d17eea
commit ae9915a713
1 changed files with 7 additions and 5 deletions
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main.py
+7 -5
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@@ -62,6 +62,10 @@ except OSError:
pass
def main():
print("#######")
print("WARNING: All rewards are clipped so you need to use a monitor (see envs.py) to get true rewards")
print("#######")
os.environ['OMP_NUM_THREADS'] = '1'
envs = SubprocVecEnv([make_env(args.env_name, args.seed, i, args.log_dir)
@@ -97,8 +101,6 @@ def main():
masks = torch.zeros(args.num_steps, args.num_processes, 1)
# These variables are used to compute average rewards for all processes.
# Note that rewards are clipped so you need to use a monitor (see envs.py)
# to get true rewards.
episode_rewards = torch.zeros([args.num_processes, 1])
final_rewards = torch.zeros([args.num_processes, 1])
@@ -138,7 +140,7 @@ def main():
update_current_state(state)
states[step + 1].copy_(current_state)
rewards[step].copy_(reward)
masks[step].copy_(torch.from_numpy(np_masks))
masks[step].copy_(torch.from_numpy(np_masks).unsqueeze(1))
final_rewards *= masks[step].cpu()
final_rewards += (1 - masks[step].cpu()) * episode_rewards
@@ -183,8 +185,8 @@ def main():
states[0].copy_(states[-1])
if j % args.log_interval == 0:
print("Updates {}, num frames {}, mean clipped reward {:.5f}, max clipped reward {:.1f}, entropy {:.5f}, value loss {:.5f}, policy loss {:.5f}".format(
j, j * args.num_processes * args.num_steps, final_rewards.mean(), final_rewards.max(), -dist_entropy.data[0], value_loss.data[0], action_loss.data[0]))
print("Updates {}, num frames {}, mean/median reward {:.1f}/{:.1f}, min/max reward {:.1f}/{:.1f}, entropy {:.5f}, value loss {:.5f}, policy loss {:.5f}".format(
j, j * args.num_processes * args.num_steps, final_rewards.mean(), final_rewards.median(), final_rewards.min(), final_rewards.max(), -dist_entropy.data[0], value_loss.data[0], action_loss.data[0]))
if __name__ == "__main__":
main()