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README.md +1 -1
args.yml +2 -2
dqn-SpaceInvadersNoFrameskip-v4.zip +2 -2
dqn-SpaceInvadersNoFrameskip-v4/data +15 -15
dqn-SpaceInvadersNoFrameskip-v4/policy.optimizer.pth +1 -1
dqn-SpaceInvadersNoFrameskip-v4/policy.pth +1 -1
replay.mp4 +2 -2
results.json +1 -1
train_eval_metrics.zip +2 -2

README.md CHANGED Viewed

@@ -16,7 +16,7 @@ model-index:
       type: SpaceInvadersNoFrameskip-v4
     metrics:
     - type: mean_reward
-      value: 572.50 +/- 81.83
       name: mean_reward
       verified: false
 ---

       type: SpaceInvadersNoFrameskip-v4
     metrics:
     - type: mean_reward
+      value: 761.50 +/- 254.57
       name: mean_reward
       verified: false
 ---

args.yml CHANGED Viewed

@@ -32,7 +32,7 @@
   - - n_startup_trials
     - 10
   - - n_timesteps
-    - 5000
   - - n_trials
     - 500
   - - no_optim_plots
@@ -54,7 +54,7 @@
   - - save_replay_buffer
     - false
   - - seed
-    - 2673070937
   - - storage
     - null
   - - study_name

   - - n_startup_trials
     - 10
   - - n_timesteps
+    - 1000000
   - - n_trials
     - 500
   - - no_optim_plots
   - - save_replay_buffer
     - false
   - - seed
+    - 2954425470
   - - storage
     - null
   - - study_name

dqn-SpaceInvadersNoFrameskip-v4.zip CHANGED Viewed

@@ -1,3 +1,3 @@
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 version https://git-lfs.github.com/spec/v1
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dqn-SpaceInvadersNoFrameskip-v4/data CHANGED Viewed

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         "__module__": "stable_baselines3.dqn.policies",
         "__doc__": "\n    Policy class for DQN when using images as input.\n\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param lr_schedule: Learning rate schedule (could be constant)\n    :param net_arch: The specification of the policy and value networks.\n    :param activation_fn: Activation function\n    :param features_extractor_class: Features extractor to use.\n    :param normalize_images: Whether to normalize images or not,\n         dividing by 255.0 (True by default)\n    :param optimizer_class: The optimizer to use,\n        ``th.optim.Adam`` by default\n    :param optimizer_kwargs: Additional keyword arguments,\n        excluding the learning rate, to pass to the optimizer\n    ",
-        "__init__": "<function CnnPolicy.__init__ at 0x7fd0d658eee0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7fd0d6589750>"
     },
     "verbose": 1,
     "policy_kwargs": {},
@@ -34,12 +34,12 @@
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     "n_envs": 1,
-    "num_timesteps": 5000,
-    "_total_timesteps": 5000,
     "_num_timesteps_at_start": 0,
     "seed": 0,
     "action_noise": null,
-    "start_time": 1672481565725537103,
     "learning_rate": {
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     "sde_sample_freq": -1,
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         "__module__": "stable_baselines3.common.buffers",
         "__doc__": "\n    Replay buffer used in off-policy algorithms like SAC/TD3.\n\n    :param buffer_size: Max number of element in the buffer\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param device: PyTorch device\n    :param n_envs: Number of parallel environments\n    :param optimize_memory_usage: Enable a memory efficient variant\n        of the replay buffer which reduces by almost a factor two the memory used,\n        at a cost of more complexity.\n        See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n        and https://github.com/DLR-RM/stable-baselines3/pull/28#issuecomment-637559274\n        Cannot be used in combination with handle_timeout_termination.\n    :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n        separately and treat the task as infinite horizon task.\n        https://github.com/DLR-RM/stable-baselines3/issues/284\n    ",
-        "__init__": "<function ReplayBuffer.__init__ at 0x7fd0d69f7e50>",
-        "add": "<function ReplayBuffer.add at 0x7fd0d69f7ee0>",
-        "sample": "<function ReplayBuffer.sample at 0x7fd0d69f7f70>",
-        "_get_samples": "<function ReplayBuffer._get_samples at 0x7fd0d69f9040>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7fd0d6a7fba0>"
     },
     "replay_buffer_kwargs": {},
     "train_freq": {
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     "exploration_fraction": 0.1,
     "target_update_interval": 1000,
-    "_n_calls": 5000,
     "max_grad_norm": 10,
     "exploration_rate": 0.01,
     "exploration_schedule": {

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+        "__init__": "<function CnnPolicy.__init__ at 0x7f5bd25a1ee0>",
         "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f5bd259c720>"
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     "verbose": 1,
     "policy_kwargs": {},
         "_shape": []
     },
     "n_envs": 1,
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+    "_total_timesteps": 1000000,
     "_num_timesteps_at_start": 0,
     "seed": 0,
     "action_noise": null,
+    "start_time": 1672481907325013196,
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     "_last_original_obs": {
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         "__module__": "stable_baselines3.common.buffers",
         "__doc__": "\n    Replay buffer used in off-policy algorithms like SAC/TD3.\n\n    :param buffer_size: Max number of element in the buffer\n    :param observation_space: Observation space\n    :param action_space: Action space\n    :param device: PyTorch device\n    :param n_envs: Number of parallel environments\n    :param optimize_memory_usage: Enable a memory efficient variant\n        of the replay buffer which reduces by almost a factor two the memory used,\n        at a cost of more complexity.\n        See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n        and https://github.com/DLR-RM/stable-baselines3/pull/28#issuecomment-637559274\n        Cannot be used in combination with handle_timeout_termination.\n    :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n        separately and treat the task as infinite horizon task.\n        https://github.com/DLR-RM/stable-baselines3/issues/284\n    ",
+        "__init__": "<function ReplayBuffer.__init__ at 0x7f5bd2a0ae50>",
+        "add": "<function ReplayBuffer.add at 0x7f5bd2a0aee0>",
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         "__abstractmethods__": "frozenset()",
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     },
     "replay_buffer_kwargs": {},
     "train_freq": {
     "exploration_final_eps": 0.01,
     "exploration_fraction": 0.1,
     "target_update_interval": 1000,
+    "_n_calls": 1000000,
     "max_grad_norm": 10,
     "exploration_rate": 0.01,
     "exploration_schedule": {

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