Upload PPO LunarLander-v2 trained agent

Henday01_Lunar_lander_v2.zip

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:b4cc0baca409261b64c327b0a98e110c5791552c04c4000aa26b461011463646
-size 147117
+oid sha256:6f08dcb88e86ed59773dde233483dcb8dacd95f5371d2b2de79eaa35da7646ad
+size 146904

Henday01_Lunar_lander_v2/data

@@ -48,11 +48,11 @@
     "seed": null,
     "action_noise": null,
     "start_time": 1670483527586847338,
-    "learning_rate": 0.0003,
+    "learning_rate": 0.012,
     "tensorboard_log": null,
     "lr_schedule": {
         ":type:": "<class 'function'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "_last_obs": {
         ":type:": "<class 'numpy.ndarray'>",
@@ -86,7 +86,7 @@
     "n_epochs": 10,
     "clip_range": {
         ":type:": "<class 'function'>",
-        ":serialized:": "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"
+        ":serialized:": "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"
     },
     "clip_range_vf": null,
     "normalize_advantage": true,

Henday01_Lunar_lander_v2/policy.optimizer.pth

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:38fd2972b87753cf6af69764c361e9f90254d06c3c8bc42c58d36d38c7f74aa8
-size 87929
+oid sha256:b2c994d98730d2b6338519baf253d478ac02157b9b999826c8d19f790908cffe
+size 88057

README.md

@@ -16,7 +16,7 @@ model-index:
       type: LunarLander-v2
     metrics:
     - type: mean_reward
-      value: 266.13 +/- 27.05
+      value: 245.49 +/- 37.98
       name: mean_reward
       verified: false
 ---

config.json

@@ -1 +1 @@
-{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x7f190029eb80>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f190029ec10>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f190029eca0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f190029ed30>", "_build": "<function ActorCriticPolicy._build at 0x7f190029edc0>", "forward": "<function ActorCriticPolicy.forward at 0x7f190029ee50>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f190029eee0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f190029ef70>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f19002a2040>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f19002a20d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f19002a2160>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f1900299b10>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1670483527586847338, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 380, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.15", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x7f190029eb80>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f190029ec10>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f190029eca0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f190029ed30>", "_build": "<function ActorCriticPolicy._build at 0x7f190029edc0>", "forward": "<function ActorCriticPolicy.forward at 0x7f190029ee50>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f190029eee0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f190029ef70>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f19002a2040>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f19002a20d0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f19002a2160>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f1900299b10>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1670483527586847338, "learning_rate": 0.012, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 380, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.15", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

results.json

@@ -1 +1 @@
-{"mean_reward": 266.13069691706045, "std_reward": 27.049396525250835, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-08T08:38:49.670296"}
+{"mean_reward": 245.49130141141114, "std_reward": 37.97633846341435, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-08T09:04:42.253640"}