First attempt at LunarLander-v2 with PPO

.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

LunarLander-sofia.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7e16d0c4f91050c66e36f7c9cbd9a3453b2a528ab9436201085f7cbfe3ac5f73
+size 144214

LunarLander-sofia/_stable_baselines3_version

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

LunarLander-sofia/data

@@ -0,0 +1,94 @@
+{
+    "policy_class": {
+        ":type:": "<class 'abc.ABCMeta'>",
+        ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
+        "__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 0x7fefd315b830>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fefd315b8c0>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fefd315b950>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fefd315b9e0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fefd315ba70>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fefd315bb00>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fefd315bb90>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fefd315bc20>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fefd315bcb0>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fefd315bd40>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fefd315bdd0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fefd31b6060>"
+    },
+    "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:": "gASVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "num_timesteps": 524288,
+    "_total_timesteps": 500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1656781646.2110171,
+    "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:": "gASVmAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSxCFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDEAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="
+    },
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": -0.04857599999999995,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 160,
+    "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
+}

LunarLander-sofia/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:22cc96b77191bb6e38448fc9aa75794006bf5e866bbc3ae8c0f0da2eae1b6fe6
+size 84893

LunarLander-sofia/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a81df3ca59dbefb7054d3ea34b198e896b81e527301dc52efa56c5246f2b0528
+size 43201

LunarLander-sofia/pytorch_variables.pth

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

LunarLander-sofia/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

README.md

@@ -0,0 +1,36 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - metrics:
+    - type: mean_reward
+      value: 233.44 +/- 34.83
+      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
+
+
+```python
+from stable_baselines3 import ...
+from huggingface_sb3 import load_from_hub
+
+...
+```

config.json

@@ -0,0 +1 @@
+{"policy_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gASVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==", "__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 0x7fefd315b830>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fefd315b8c0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fefd315b950>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fefd315b9e0>", "_build": "<function ActorCriticPolicy._build at 0x7fefd315ba70>", "forward": "<function ActorCriticPolicy.forward at 0x7fefd315bb00>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fefd315bb90>", "_predict": "<function ActorCriticPolicy._predict at 0x7fefd315bc20>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fefd315bcb0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fefd315bd40>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fefd315bdd0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fefd31b6060>"}, "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:": "gASVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 524288, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1656781646.2110171, "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:": "gASVmAAAAAAAAACMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMDF9yZWNvbnN0cnVjdJSTlIwFbnVtcHmUjAduZGFycmF5lJOUSwCFlEMBYpSHlFKUKEsBSxCFlGgDjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYolDEAAAAAAAAAAAAAAAAAAAAACUdJRiLg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.04857599999999995, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 160, "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-5.4.188+-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:bb88bc14cb997039ec62c1028a203d57075bd34bdc8ec95e674182b80c06741d
+size 239272

results.json

@@ -0,0 +1 @@
+{"mean_reward": 233.43994290025557, "std_reward": 34.82776578256133, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-07-02T17:23:15.147600"}