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a2c-PandaReachDense-v2

Reinforcement Learning
stable-baselines3
PandaReachDense-v2
deep-reinforcement-learning
Eval Results
Model card Files Community
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  1. README.md +1 -1
  2. a2c-PandaReachDense-v2.zip +2 -2
  3. a2c-PandaReachDense-v2/data +15 -15
  4. a2c-PandaReachDense-v2/policy.optimizer.pth +1 -1
  5. a2c-PandaReachDense-v2/policy.pth +1 -1
  6. config.json +1 -1
  7. replay.mp4 +0 -0
  8. results.json +1 -1
  9. vec_normalize.pkl +1 -1
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@@ -16,7 +16,7 @@ model-index:
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  type: PandaReachDense-v2
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  metrics:
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  - type: mean_reward
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- value: -4.59 +/- 1.28
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  name: mean_reward
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  verified: false
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  ---
 
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  type: PandaReachDense-v2
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  metrics:
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  - type: mean_reward
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  name: mean_reward
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  verified: false
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  ---
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  "__doc__": "\n MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction).\n Used by A2C, PPO and the likes.\n\n :param observation_space: Observation space (Tuple)\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 ortho_init: Whether to use or not orthogonal initialization\n :param use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param full_std: Whether to use (n_features x n_actions) parameters\n for the std instead of only (n_features,) when using gSDE\n :param use_expln: Use ``expln()`` function instead of ``exp()`` to ensure\n a positive standard deviation (cf paper). It allows to keep variance\n above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.\n :param squash_output: Whether to squash the output using a tanh function,\n this allows to ensure boundaries when using gSDE.\n :param features_extractor_class: Uses the CombinedExtractor\n :param features_extractor_kwargs: Keyword arguments\n to pass to the features extractor.\n :param share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 ",
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+ "__init__": "<function MultiInputActorCriticPolicy.__init__ at 0x7f8e608ba670>",
8
  "__abstractmethods__": "frozenset()",
9
+ "_abc_impl": "<_abc._abc_data object at 0x7f8e608bb0c0>"
10
  },
11
  "verbose": 1,
12
  "policy_kwargs": {
 
40
  "bounded_above": "[ True True True]",
41
  "_np_random": null
42
  },
43
+ "n_envs": 100,
44
  "num_timesteps": 1000000,
45
  "_total_timesteps": 1000000,
46
  "_num_timesteps_at_start": 0,
47
  "seed": null,
48
  "action_noise": null,
49
+ "start_time": 1678513971691228670,
50
  "learning_rate": 0.0007,
51
  "tensorboard_log": null,
52
  "lr_schedule": {
 
55
  },
56
  "_last_obs": {
57
  ":type:": "<class 'collections.OrderedDict'>",
58
+ ":serialized:": 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