Initial commit

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: -188.60 +/- 65.20
+      value: 173.57 +/- 138.27
       name: mean_reward
       verified: false
 ---

args.yml

@@ -9,6 +9,8 @@
     - LunarLander-v2
   - - env_kwargs
     - null
+  - - eval_env_kwargs
+    - null
   - - eval_episodes
     - 5
   - - eval_freq
@@ -54,7 +56,7 @@
   - - save_replay_buffer
     - false
   - - seed
-    - 43775958
+    - 3661468958
   - - storage
     - null
   - - study_name

dqn-LunarLander-v2.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
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-size 1133414
+oid sha256:a98a18f0b5398cfdb758d719f02a2daf60391f94872cf53b88750b8bdfb7f765
+size 1134391

dqn-LunarLander-v2/_stable_baselines3_version

@@ -1 +1 @@
-2.1.0
+2.3.2

dqn-LunarLander-v2/data

@@ -5,15 +5,15 @@
         "__module__": "stable_baselines3.dqn.policies",
         "__annotations__": "{'q_net': <class 'stable_baselines3.dqn.policies.QNetwork'>, 'q_net_target': <class 'stable_baselines3.dqn.policies.QNetwork'>}",
         "__doc__": "\n    Policy class with Q-Value Net and target net for DQN\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 features_extractor_kwargs: Keyword arguments\n        to pass to the features extractor.\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 DQNPolicy.__init__ at 0x7efe1aa00550>",
-        "_build": "<function DQNPolicy._build at 0x7efe1aa005e0>",
-        "make_q_net": "<function DQNPolicy.make_q_net at 0x7efe1aa00670>",
-        "forward": "<function DQNPolicy.forward at 0x7efe1aa00700>",
-        "_predict": "<function DQNPolicy._predict at 0x7efe1aa00790>",
-        "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7efe1aa00820>",
-        "set_training_mode": "<function DQNPolicy.set_training_mode at 0x7efe1aa008b0>",
+        "__init__": "<function DQNPolicy.__init__ at 0x7f6f0281a8b0>",
+        "_build": "<function DQNPolicy._build at 0x7f6f0281a940>",
+        "make_q_net": "<function DQNPolicy.make_q_net at 0x7f6f0281a9d0>",
+        "forward": "<function DQNPolicy.forward at 0x7f6f0281aa60>",
+        "_predict": "<function DQNPolicy._predict at 0x7f6f0281aaf0>",
+        "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7f6f0281ab80>",
+        "set_training_mode": "<function DQNPolicy.set_training_mode at 0x7f6f0281ac10>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7efe1aa03940>"
+        "_abc_impl": "<_abc._abc_data object at 0x7f6f0281da40>"
     },
     "verbose": 1,
     "policy_kwargs": {
@@ -27,10 +27,10 @@
     "_num_timesteps_at_start": 0,
     "seed": 0,
     "action_noise": null,
-    "start_time": 1721095875529543112,
+    "start_time": 1721099501012247454,
     "learning_rate": {
         ":type:": "<class 'function'>",
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     },
     "tensorboard_log": null,
     "_last_obs": null,
@@ -40,16 +40,16 @@
     },
     "_last_original_obs": {
         ":type:": "<class 'numpy.ndarray'>",
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     },
-    "_episode_num": 537,
+    "_episode_num": 238,
     "use_sde": false,
     "sde_sample_freq": -1,
     "_current_progress_remaining": 0.0,
     "_stats_window_size": 100,
     "ep_info_buffer": {
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     },
     "ep_success_buffer": {
         ":type:": "<class 'collections.deque'>",
@@ -92,14 +92,15 @@
         ":type:": "<class 'abc.ABCMeta'>",
         ":serialized:": "gAWVNQAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwMUmVwbGF5QnVmZmVylJOULg==",
         "__module__": "stable_baselines3.common.buffers",
+        "__annotations__": "{'observations': <class 'numpy.ndarray'>, 'next_observations': <class 'numpy.ndarray'>, 'actions': <class 'numpy.ndarray'>, 'rewards': <class 'numpy.ndarray'>, 'dones': <class 'numpy.ndarray'>, 'timeouts': <class 'numpy.ndarray'>}",
         "__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 0x7efe1aa74b80>",
-        "add": "<function ReplayBuffer.add at 0x7efe1aa74c10>",
-        "sample": "<function ReplayBuffer.sample at 0x7efe1aa74ca0>",
-        "_get_samples": "<function ReplayBuffer._get_samples at 0x7efe1aa74d30>",
-        "_maybe_cast_dtype": "<staticmethod object at 0x7efe1aaf0e20>",
+        "__init__": "<function ReplayBuffer.__init__ at 0x7f6f029320d0>",
+        "add": "<function ReplayBuffer.add at 0x7f6f02932160>",
+        "sample": "<function ReplayBuffer.sample at 0x7f6f029321f0>",
+        "_get_samples": "<function ReplayBuffer._get_samples at 0x7f6f02932280>",
+        "_maybe_cast_dtype": "<staticmethod object at 0x7f6f029b50a0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc._abc_data object at 0x7efe1aa6c600>"
+        "_abc_impl": "<_abc._abc_data object at 0x7f6f02985400>"
     },
     "replay_buffer_kwargs": {},
     "train_freq": {
@@ -116,12 +117,12 @@
     "exploration_rate": 0.1,
     "lr_schedule": {
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