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CartPole-v1-CP_DQPN_x1-seed3

Reinforcement Learning
TensorBoard
cleanrl
CartPole-v1
deep-reinforcement-learning
custom-implementation
Eval Results
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mmorales34 commited on
Commit
aa4480b
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1 Parent(s): b085d98

pushing model

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Files changed (5) hide show
  1. dqpn_freq.py +3 -4
  2. events.out.tfevents.1679277760.rhea.183515.0 → events.out.tfevents.1679289431.redi.466821.0 +1 -1
  3. videos/{CartPole-v1__CP_DQPN_x1__3__1679277756-eval → CartPole-v1__CP_DQPN_x1__3__1679289427-eval}/rl-video-episode-0.mp4 +0 -0
  4. videos/{CartPole-v1__CP_DQPN_x1__3__1679277756-eval → CartPole-v1__CP_DQPN_x1__3__1679289427-eval}/rl-video-episode-1.mp4 +0 -0
  5. videos/{CartPole-v1__CP_DQPN_x1__3__1679277756-eval → CartPole-v1__CP_DQPN_x1__3__1679289427-eval}/rl-video-episode-8.mp4 +0 -0
dqpn_freq.py CHANGED
@@ -141,17 +141,16 @@ if __name__ == "__main__":
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  wandb.log({name: x, "global_step": y})
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  # TRY NOT TO MODIFY: seeding
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- random.seed(args.seed)
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- np.random.seed(args.seed)
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  torch.manual_seed(args.seed)
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- torch.backends.cudnn.deterministic = args.torch_deterministic
 
 
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  device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
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  # env setup
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  envs = gym.vector.SyncVectorEnv([make_env(args.env_id, args.seed, 0, args.capture_video, run_name)])
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  assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
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- envs.seed(args.seed)
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  q_network = QNetwork(envs).to(device)
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  optimizer = optim.RMSprop(q_network.parameters(), lr=args.learning_rate)
 
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  wandb.log({name: x, "global_step": y})
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  # TRY NOT TO MODIFY: seeding
 
 
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  torch.manual_seed(args.seed)
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+ # torch.backends.cudnn.deterministic = args.torch_deterministic
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+ np.random.seed(args.seed)
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+ random.seed(args.seed)
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  device = torch.device("cuda" if torch.cuda.is_available() and args.cuda else "cpu")
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  # env setup
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  envs = gym.vector.SyncVectorEnv([make_env(args.env_id, args.seed, 0, args.capture_video, run_name)])
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  assert isinstance(envs.single_action_space, gym.spaces.Discrete), "only discrete action space is supported"
 
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  q_network = QNetwork(envs).to(device)
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  optimizer = optim.RMSprop(q_network.parameters(), lr=args.learning_rate)
events.out.tfevents.1679277760.rhea.183515.0 → events.out.tfevents.1679289431.redi.466821.0 RENAMED
@@ -1,3 +1,3 @@
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  version https://git-lfs.github.com/spec/v1
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- oid sha256:6bab88546ee1c7094baaaeda13ebb4684239806aba8b6249d21d26b7993ce475
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  size 684
 
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  version https://git-lfs.github.com/spec/v1
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+ oid sha256:d7f9f851c34b5a4bd724939e309f3f0326129819727edfe81190ec4d3630443b
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  size 684
videos/{CartPole-v1__CP_DQPN_x1__3__1679277756-eval → CartPole-v1__CP_DQPN_x1__3__1679289427-eval}/rl-video-episode-0.mp4 RENAMED
File without changes
videos/{CartPole-v1__CP_DQPN_x1__3__1679277756-eval → CartPole-v1__CP_DQPN_x1__3__1679289427-eval}/rl-video-episode-1.mp4 RENAMED
File without changes
videos/{CartPole-v1__CP_DQPN_x1__3__1679277756-eval → CartPole-v1__CP_DQPN_x1__3__1679289427-eval}/rl-video-episode-8.mp4 RENAMED
File without changes