Initial Commit

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: -112.60 +/- 24.36
+      value: -103.40 +/- 7.49
       name: mean_reward
     task:
       type: reinforcement-learning

args.yml

@@ -1,28 +1,24 @@
 !!python/object/apply:collections.OrderedDict
 - - - algo
     - dqn
-  - - device
-    - auto
   - - env
     - MountainCar-v0
   - - env_kwargs
     - null
   - - eval_episodes
-    - 5
+    - 10
   - - eval_freq
-    - 25000
+    - 10000
   - - gym_packages
     - []
   - - hyperparams
     - null
   - - log_folder
-    - logs
+    - rl-trained-agents/
   - - log_interval
     - -1
-  - - n_eval_envs
-    - 1
   - - n_evaluations
-    - null
+    - 20
   - - n_jobs
     - 1
   - - n_startup_trials
@@ -30,13 +26,9 @@
   - - n_timesteps
     - -1
   - - n_trials
-    - 500
-  - - no_optim_plots
-    - false
+    - 10
   - - num_threads
     - -1
-  - - optimization_log_path
-    - null
   - - optimize_hyperparameters
     - false
   - - pruner
@@ -48,26 +40,20 @@
   - - save_replay_buffer
     - false
   - - seed
-    - 2005659129
+    - 1787207996
   - - storage
     - null
   - - study_name
     - null
   - - tensorboard_log
     - ''
-  - - track
-    - false
   - - trained_agent
     - ''
   - - truncate_last_trajectory
     - true
   - - uuid
-    - false
+    - true
   - - vec_env
     - dummy
   - - verbose
     - 1
-  - - wandb_entity
-    - null
-  - - wandb_project_name
-    - sb3

dqn-MountainCar-v0.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:9991681e4b2383b8978b2dc7c3092287654f968da9a631511fe5c7f905d3ef77
-size 1103671
+oid sha256:c5d02d795e9120efb19cd1f65541a7f040eacf9b4253bfd438ebc35b64113a14
+size 1103767

dqn-MountainCar-v0/data

@@ -4,15 +4,15 @@
         ":serialized:": "gAWVMAAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLmRxbi5wb2xpY2llc5SMCURRTlBvbGljeZSTlC4=",
         "__module__": "stable_baselines3.dqn.policies",
         "__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 0x7f0d327ddb00>",
-        "_build": "<function DQNPolicy._build at 0x7f0d327ddb90>",
-        "make_q_net": "<function DQNPolicy.make_q_net at 0x7f0d327ddc20>",
-        "forward": "<function DQNPolicy.forward at 0x7f0d327ddcb0>",
-        "_predict": "<function DQNPolicy._predict at 0x7f0d327ddd40>",
-        "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7f0d327dddd0>",
-        "set_training_mode": "<function DQNPolicy.set_training_mode at 0x7f0d327dde60>",
+        "__init__": "<function DQNPolicy.__init__ at 0x7fd067581b00>",
+        "_build": "<function DQNPolicy._build at 0x7fd067581b90>",
+        "make_q_net": "<function DQNPolicy.make_q_net at 0x7fd067581c20>",
+        "forward": "<function DQNPolicy.forward at 0x7fd067581cb0>",
+        "_predict": "<function DQNPolicy._predict at 0x7fd067581d40>",
+        "_get_constructor_parameters": "<function DQNPolicy._get_constructor_parameters at 0x7fd067581dd0>",
+        "set_training_mode": "<function DQNPolicy.set_training_mode at 0x7fd067581e60>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f0d327d64b0>"
+        "_abc_impl": "<_abc_data object at 0x7fd0675794e0>"
     },
     "verbose": 1,
     "policy_kwargs": {
@@ -23,24 +23,24 @@
     },
     "observation_space": {
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         "dtype": "float32",
-        "_shape": [
-            2
-        ],
         "low": "[-1.2  -0.07]",
         "high": "[0.6  0.07]",
         "bounded_below": "[ True  True]",
         "bounded_above": "[ True  True]",
-        "_np_random": null
+        "_np_random": null,
+        "_shape": [
+            2
+        ]
     },
     "action_space": {
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-        "_shape": [],
         "dtype": "int64",
-        "_np_random": "RandomState(MT19937)"
+        "_np_random": "RandomState(MT19937)",
+        "_shape": []
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     "n_envs": 1,
     "num_timesteps": 120000,
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     "seed": 0,
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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:\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    :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    ",
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-        "sample": "<function ReplayBuffer.sample at 0x7f0d32c43680>",
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+        "add": "<function ReplayBuffer.add at 0x7fd0679f2f80>",
+        "sample": "<function ReplayBuffer.sample at 0x7fd0679e7680>",
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