Test

.gitattributes

@@ -29,3 +29,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zstandard filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+replay.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: 86.19 +/- 62.44
+      value: 1993.03 +/- 11.92
       name: mean_reward
     task:
       type: reinforcement-learning

config.json

@@ -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 0x7f5dd8545ef0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5dd8545f80>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5dd854d050>", "_build_mlp_extractor": "<function 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ppo-Walker2DBulletEnv-v0/data

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         "__module__": "stable_baselines3.common.policies",
         "__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    ",
-        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f5dd8545ef0>",
-        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f5dd8545f80>",
-        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f5dd854d050>",
-        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f5dd854d0e0>",
-        "_build": "<function ActorCriticPolicy._build at 0x7f5dd854d170>",
-        "forward": "<function ActorCriticPolicy.forward at 0x7f5dd854d200>",
-        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f5dd854d290>",
-        "_predict": "<function ActorCriticPolicy._predict at 0x7f5dd854d320>",
-        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f5dd854d3b0>",
-        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f5dd854d440>",
-        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f5dd854d4d0>",
+        "__init__": "<function ActorCriticPolicy.__init__ at 0x7f7902214200>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7902214290>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7902214320>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f79022143b0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f7902214440>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f79022144d0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f7902214560>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f79022145f0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f7902214680>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f7902214710>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f79022147a0>",
         "__abstractmethods__": "frozenset()",
-        "_abc_impl": "<_abc_data object at 0x7f5dd8586f00>"
+        "_abc_impl": "<_abc_data object at 0x7f7902257d20>"
     },
     "verbose": 1,
     "policy_kwargs": {
@@ -65,12 +65,12 @@
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     "n_envs": 16,
-    "num_timesteps": 16384,
-    "_total_timesteps": 10000,
+    "num_timesteps": 1007616,
+    "_total_timesteps": 1000000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
-    "start_time": 1658403505.294959,
+    "start_time": 1658409884.222429,
     "learning_rate": 3e-05,
     "tensorboard_log": "./tensorboard",
     "lr_schedule": {
@@ -79,29 +79,29 @@
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ppo-Walker2DBulletEnv-v0/system_info.txt

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 Python: 3.7.13
 Stable-Baselines3: 1.6.0
 PyTorch: 1.12.0+cu113
-GPU Enabled: False
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 Numpy: 1.21.6
 Gym: 0.21.0

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