Upload PPO Lunarland trained agent

.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: 243.82 +/- 48.25
+      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 0x7f96dfe579e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f96dfe57a70>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f96dfe57b00>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f96dfe57b90>", "_build": "<function ActorCriticPolicy._build at 0x7f96dfe57c20>", "forward": "<function ActorCriticPolicy.forward at 0x7f96dfe57cb0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f96dfe57d40>", "_predict": "<function ActorCriticPolicy._predict at 0x7f96dfe57dd0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f96dfe57e60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f96dfe57ef0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f96dfe57f80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f96dfe9bd20>"}, "verbose": 0, "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": 1652912377.6881115, "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": 310, "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:": "gAWVvwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwNX2J1aWx0aW5fdHlwZZSTlIwKTGFtYmRhVHlwZZSFlFKUKGgCjAhDb2RlVHlwZZSFlFKUKEsBSwBLAUsBSxNDBIgAUwCUToWUKYwBX5SFlIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5RLgEMCAAGUjAN2YWyUhZQpdJRSlH2UKIwLX19wYWNrYWdlX1+UjBhzdGFibGVfYmFzZWxpbmVzMy5jb21tb26UjAhfX25hbWVfX5SMHnN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi51dGlsc5SMCF9fZmlsZV9flIxIL3Vzci9sb2NhbC9saWIvcHl0aG9uMy43L2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaCB9lH2UKGgXaA6MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgYjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "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": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aaf104302f1b8e0a70dc2dceb0153ed603b1f1c5df498b6549fb170307ba1965
+size 144017

ppo-LunarLander-v2/_stable_baselines3_version

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

ppo-LunarLander-v2/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 0x7f96dfe579e0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f96dfe57a70>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f96dfe57b00>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f96dfe57b90>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f96dfe57c20>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f96dfe57cb0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f96dfe57d40>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f96dfe57dd0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f96dfe57e60>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f96dfe57ef0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f96dfe57f80>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f96dfe9bd20>"
+    },
+    "verbose": 0,
+    "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": 1652912377.6881115,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAO3+Or7IzrC8mGXOOs/BMzmVFBs+vmUJugAAgD8AAIA/mustPI8/Gj67awa9+s+hvri6sLs1qYy8AAAAAAAAAABm9kI8KRh6uloBZLkAW320Zz5YOrpzgjgAAIA/AACAP4NckD7auQA/F/22PYwNBb9iriA+/iZVvQAAAAAAAAAAU01GPuG7obwVeNo5+10TuNAhEL53Ggq5AACAPwAAgD+azTO8ZS+sP/s11b3G4Ny+kXvxu2Imrb0AAAAAAAAAAM1xg7xCOK8/9olkvvOZsb4lDu06bDemvAAAAAAAAAAAsOZOvgGfzrwo3cC7aQ+uPMn9IT7b6oM9AACAPwAAgD+GWCI+j4h7vJNiJDtxIL655UjVveG9nLoAAIA/AACAP7Nenz2U+oE/fU1EPqcxUb/26Jc9QVESPQAAAAAAAAAAzW7jvcPpIbrLvc25hkKbtFVnjDtYJfQ4AACAPwAAAACTKF6+q2trP9hsqr5lC9G+2hN+vka7rr0AAAAAAAAAABOaRb5bBLm8B5gnO614mTlu9CQ+omFbugAAgD8AAIA/E1avPjKy2T5SsEe9ta75vinPGz7KD/m9AAAAAAAAAAAzwd08V4CYP2zLIT7EQji/nTEFPXIVJT0AAAAAAAAAAPNHFz668O4+m3GZvTZbCL9feBs+Om2LvQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="
+    },
+    "_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": 310,
+    "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
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1ef20fa7ce3d7cc13b1d9f828f7e01c502a41548d82aa23744cb15e6b64c2a5e
+size 84893

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:172bafc75021fe3221e9e443bad44bc11e2c7d0aab6d7657cbdcbf16efefbc59
+size 43201

ppo-LunarLander-v2/pytorch_variables.pth

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

ppo-LunarLander-v2/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: True
+Numpy: 1.21.6
+Gym: 0.21.0

replay.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:610870494449b9d13cd2bf056daa0e24ee2cb804b4899dae0c8929eb6742d876
+size 251802

results.json

@@ -0,0 +1 @@
+{"mean_reward": 243.81680859702396, "std_reward": 48.25195364246476, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-18T22:57:24.630395"}