uploading the trained model

.gitattributes

@@ -25,3 +25,4 @@ 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
+*.mp4 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,28 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 289.86 +/- 15.74
+      name: mean_reward
+    task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+---
+
+  # **PPO** Agent playing **LunarLander-v2**
+  This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
+  
+  ## Usage (with Stable-baselines3)
+  TODO: Add your code
+  

config.json

@@ -0,0 +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 0x7fca0eee3b00>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fca0eee3b90>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fca0eee3c20>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fca0eee3cb0>", "_build": "<function ActorCriticPolicy._build at 0x7fca0eee3d40>", "forward": "<function ActorCriticPolicy.forward at 0x7fca0eee3dd0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fca0eee3e60>", "_predict": "<function ActorCriticPolicy._predict at 0x7fca0eee3ef0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fca0eee3f80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fca0eee8050>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fca0eee80e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fca0eeba2a0>"}, "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": 9011200, "_total_timesteps": 9000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1652208115.7804134, "learning_rate": 0.003, "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": -0.0012444444444443814, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 2200, "n_steps": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 32, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cu113", "GPU Enabled": "False", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-LunarLander-v2-try3.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:03d38143d83ed76e3627a73dbf427b862565f4d232f2c91698632360cdfa5438
+size 143604

ppo-LunarLander-v2-try3/_stable_baselines3_version

@@ -0,0 +1 @@
+1.5.0

ppo-LunarLander-v2-try3/data

@@ -0,0 +1,94 @@
+{
+    "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 0x7fca0eee3b00>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fca0eee3b90>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fca0eee3c20>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fca0eee3cb0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fca0eee3d40>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fca0eee3dd0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fca0eee3e60>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fca0eee3ef0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fca0eee3f80>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fca0eee8050>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fca0eee80e0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fca0eeba2a0>"
+    },
+    "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": 9011200,
+    "_total_timesteps": 9000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1652208115.7804134,
+    "learning_rate": 0.003,
+    "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": -0.0012444444444443814,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 2200,
+    "n_steps": 1024,
+    "gamma": 0.999,
+    "gae_lambda": 0.98,
+    "ent_coef": 0.01,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 32,
+    "n_epochs": 4,
+    "clip_range": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

ppo-LunarLander-v2-try3/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:da293e3dda6f16a5f1b53cad537feac36d5f25419ca289511107930c0f8326e8
+size 84637

ppo-LunarLander-v2-try3/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8ee8ecf136f1641de26dde272ef466c45b9517b28a3c5684a21e1c7818925f66
+size 43073

ppo-LunarLander-v2-try3/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
+size 431

ppo-LunarLander-v2-try3/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
+Python: 3.7.13
+Stable-Baselines3: 1.5.0
+PyTorch: 1.11.0+cu113
+GPU Enabled: False
+Numpy: 1.21.6
+Gym: 0.21.0

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:caeccaaa9c45fcd4fbffc678cbdda7f6926b11662e94833fe86250dec147077f
+size 193079

results.json

@@ -0,0 +1 @@
+{"mean_reward": 289.8576667560677, "std_reward": 15.736908287470888, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-10T20:49:08.791542"}