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
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-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 @@
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  "observation_space": {
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  "low": "[-1.2 -0.07]",
  "high": "[0.6 0.07]",
  "bounded_below": "[ True True]",
  "bounded_above": "[ True True]",
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  "action_space": {
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  "train_freq": {
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  "exploration_fraction": 0.2,
  "target_update_interval": 600,
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 }

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replay.mp4

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results.json

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train_eval_metrics.zip

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