with learning_rate=0.000001, gamma=0.0001, clip_range=0.01, 10e5 iterations

Browse files

Files changed (9) hide show

README.md +1 -1
config.json +1 -1
replay.mp4 +2 -2
results.json +1 -1
rl-course-unit-1-ppo-lunarlander-v2.zip +2 -2
rl-course-unit-1-ppo-lunarlander-v2/data +24 -24
rl-course-unit-1-ppo-lunarlander-v2/policy.optimizer.pth +2 -2
rl-course-unit-1-ppo-lunarlander-v2/policy.pth +2 -2
rl-course-unit-1-ppo-lunarlander-v2/system_info.txt +5 -5

README.md CHANGED Viewed

@@ -10,7 +10,7 @@ model-index:
   results:
   - metrics:
     - type: mean_reward
-      value: -70.05 +/- 78.89
       name: mean_reward
     task:
       type: reinforcement-learning

   results:
   - metrics:
     - type: mean_reward
+      value: -115.60 +/- 71.46
       name: mean_reward
     task:
       type: reinforcement-learning

config.json CHANGED Viewed

@@ -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 0x7f7ad9d60290>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f7ad9d60320>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f7ad9d603b0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f7ad9d60440>", "_build": "<function 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@@ -77,7 +77,7 @@
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     "_n_updates": 10,
     "n_steps": 2048,
-    "gamma": 0.001,
     "gae_lambda": 0.95,
     "ent_coef": 0.0,
     "vf_coef": 0.5,
@@ -86,7 +86,7 @@
     "n_epochs": 10,
     "clip_range": {
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     "normalize_advantage": true,

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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 0x7f8c9c1391f0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8c9c139280>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8c9c139310>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8c9c1393a0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f8c9c139430>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f8c9c1394c0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f8c9c139550>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f8c9c1395e0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f8c9c139670>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f8c9c139700>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f8c9c139790>",
         "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7f8c9c13a3c0>"
     },
     "verbose": 1,
     "policy_kwargs": {},
         "dtype": "int64",
         "_np_random": null
     },
+    "n_envs": 1000,
+    "num_timesteps": 2048000,
+    "_total_timesteps": 100000,
     "_num_timesteps_at_start": 0,
     "seed": null,
     "action_noise": null,
+    "start_time": 1651761724.580514,
+    "learning_rate": 1e-06,
     "tensorboard_log": null,
     "lr_schedule": {
         ":type:": "<class 'function'>",
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     },
     "_last_obs": {
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