cj-mills commited on
Commit
8d0565c
1 Parent(s): 70eed62

Upload ppo-LunarLander-v2 model with longer training session

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README.md CHANGED
@@ -10,7 +10,7 @@ model-index:
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  results:
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  - metrics:
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  - type: mean_reward
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- value: 142.05 +/- 77.13
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  name: mean_reward
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  task:
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  type: reinforcement-learning
 
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  results:
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  - metrics:
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  - type: mean_reward
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+ value: 267.22 +/- 15.38
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  name: mean_reward
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  task:
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  type: reinforcement-learning
config.json CHANGED
@@ -1 +1 @@
1
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If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\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: 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 ActorCriticPolicy.__init__ at 0x7fa789c20dc0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa789c20e50>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa789c20ee0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa789c20f70>", "_build": "<function 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  },
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@@ -78,8 +78,8 @@
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  "_n_updates": 310,
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  "n_steps": 2048,
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  "gamma": 0.999,
81
- "gae_lambda": 0.98,
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- "ent_coef": 0.01,
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  "vf_coef": 0.5,
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  "max_grad_norm": 0.5,
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  "batch_size": 32,
 
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  "__module__": "stable_baselines3.common.policies",
6
  "__doc__": "\n 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\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 sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\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: 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 ",
7
+ "__init__": "<function ActorCriticPolicy.__init__ at 0x7fa6c81dedc0>",
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+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa6c81dee50>",
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+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa6c81deee0>",
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+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa6c81def70>",
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+ "_build": "<function ActorCriticPolicy._build at 0x7fa6c81e2040>",
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+ "forward": "<function ActorCriticPolicy.forward at 0x7fa6c81e20d0>",
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+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa6c81e2160>",
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+ "_predict": "<function ActorCriticPolicy._predict at 0x7fa6c81e21f0>",
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+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa6c81e2280>",
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+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa6c81e2310>",
17
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa6c81e23a0>",
18
  "__abstractmethods__": "frozenset()",
19
+ "_abc_impl": "<_abc._abc_data object at 0x7fa6c81e4200>"
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  },
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  "verbose": 1,
22
  "policy_kwargs": {},
 
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  "_num_timesteps_at_start": 0,
48
  "seed": null,
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  "action_noise": null,
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+ "start_time": 1652401953.8535538,
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  "learning_rate": 0.0003,
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  "tensorboard_log": null,
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  "lr_schedule": {
 
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  },
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  "_last_obs": {
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  },
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  "_last_episode_starts": {
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  "ep_info_buffer": {
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