First commit

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: PPO
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 241.92 +/- 49.59
+      name: mean_reward
+      verified: false
+---
+
+# **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:": "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 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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7baec9ab93f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7baec9ab9480>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7baec9ab9510>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7baec9ab95a0>", "_build": "<function ActorCriticPolicy._build at 0x7baec9ab9630>", "forward": "<function ActorCriticPolicy.forward at 0x7baec9ab96c0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7baec9ab9750>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7baec9ab97e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7baec9ab9870>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7baec9ab9900>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7baec9ab9990>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7baec9ab9a20>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7baec9a48400>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1719830046043776321, "learning_rate": 0.0003, "tensorboard_log": null, "_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, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 310, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "gAWVdgIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWCAAAAAAAAAABAQEBAQEBAZRoCIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksIhZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoESiWCAAAAAAAAAABAQEBAQEBAZRoFUsIhZRoGXSUUpSMBl9zaGFwZZRLCIWUjANsb3eUaBEoliAAAAAAAAAAAAC0wgAAtMIAAKDAAACgwNsPScAAAKDAAAAAgAAAAICUaAtLCIWUaBl0lFKUjARoaWdolGgRKJYgAAAAAAAAAAAAtEIAALRCAACgQAAAoEDbD0lAAACgQAAAgD8AAIA/lGgLSwiFlGgZdJRSlIwIbG93X3JlcHKUjFtbLTkwLiAgICAgICAgLTkwLiAgICAgICAgIC01LiAgICAgICAgIC01LiAgICAgICAgIC0zLjE0MTU5MjcgIC01LgogIC0wLiAgICAgICAgIC0wLiAgICAgICBdlIwJaGlnaF9yZXBylIxTWzkwLiAgICAgICAgOTAuICAgICAgICAgNS4gICAgICAgICA1LiAgICAgICAgIDMuMTQxNTkyNyAgNS4KICAxLiAgICAgICAgIDEuICAgICAgIF2UjApfbnBfcmFuZG9tlE51Yi4=", "dtype": "float32", "bounded_below": "[ True  True  True  True  True  True  True  True]", "bounded_above": "[ True  True  True  True  True  True  True  True]", "_shape": [8], "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]", "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "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:": "gAWVxQIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTAvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUjARmdW5jlEuEQwIEAZSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEwL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lHVOTmgAjBBfbWFrZV9lbXB0eV9jZWxslJOUKVKUhZR0lFKUjBxjbG91ZHBpY2tsZS5jbG91ZHBpY2tsZV9mYXN0lIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaB99lH2UKGgWaA2MDF9fcXVhbG5hbWVfX5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUjA9fX2Fubm90YXRpb25zX1+UfZSMDl9fa3dkZWZhdWx0c19flE6MDF9fZGVmYXVsdHNfX5ROjApfX21vZHVsZV9flGgXjAdfX2RvY19flE6MC19fY2xvc3VyZV9flGgAjApfbWFrZV9jZWxslJOURz/JmZmZmZmahZRSlIWUjBdfY2xvdWRwaWNrbGVfc3VibW9kdWxlc5RdlIwLX19nbG9iYWxzX1+UfZR1hpSGUjAu"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Tue Jun 18 14:18:04 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.3.0+cu121", "GPU Enabled": "True", "Numpy": "1.25.2", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7323e5297fee85c1072565cbc2a02661867c1260e7cc05853e8bbdc168a7c69b
+size 147983

ppo-LunarLander-v2/_stable_baselines3_version

@@ -0,0 +1 @@
+2.0.0a5

ppo-LunarLander-v2/data

@@ -0,0 +1,99 @@
+{
+    "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 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 share_features_extractor: If True, the features extractor is shared between the policy and value networks.\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 0x7baec9ab93f0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7baec9ab9480>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7baec9ab9510>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7baec9ab95a0>",
+        "_build": "<function ActorCriticPolicy._build at 0x7baec9ab9630>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7baec9ab96c0>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x7baec9ab9750>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7baec9ab97e0>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7baec9ab9870>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7baec9ab9900>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7baec9ab9990>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7baec9ab9a20>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x7baec9a48400>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1719830046043776321,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "_last_obs": {
+        ":type:": "<class 'numpy.ndarray'>",
+        ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAAD8uz2fqYK7gg4dvfSNEDyeLLc8T4sAvQAAgD8AAIA/TbRGPvt9zj5e/468LpDJvuCKyD0TPNK9AAAAAAAAAAANuLE9XDMDuguLFDircbgyOfJyuSarKrcAAIA/AACAP02v+L1xiiK7J/e3vJmPBLsXmR482JnlOwAAgD8AAIA/ZqJvvZQjNz+95u+8usIMv/DhYr2Vxas8AAAAAAAAAABKAaI+Bfi7Pl9Csb5MiNa+vscMOiEZyL0AAAAAAAAAAADRET2DYSO8LVhevPOjdz069NY8G2NYOgAAgD8AAIA/s01mvcPZarpq6j27WcRbOd8DxLpA8N02AACAPwAAgD/6L68+kV2OP2u/7j5WnxK/NnbFPq1H4zoAAAAAAAAAALpRJr72x2i85UosOx880DmXQ889CuOcugAAgD8AAIA/TcH6PTLsoz9aCm4+B0cwvyiHrj03hJU9AAAAAAAAAABzxAK+KLqSPzM1U76lzz6/bVGzvQvnBr0AAAAAAAAAAAjZwL7W51U/1wervhzWLL8g9pq+gDT4uQAAAAAAAAAAzfCDPfbsZLroqdoyaWbrMHJJ37pC9YezAACAPwAAgD8DlYi+Tqn4PY0MlT4FS4C+fnXSvOUACT4AAAAAAAAAAOhjoL5yCC0+YUwNPvEJG7/Jfrm+8V8PPgAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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,
+    "_stats_window_size": 100,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 310,
+    "observation_space": {
+        ":type:": "<class 'gymnasium.spaces.box.Box'>",
+        ":serialized:": "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",
+        "dtype": "float32",
+        "bounded_below": "[ True  True  True  True  True  True  True  True]",
+        "bounded_above": "[ True  True  True  True  True  True  True  True]",
+        "_shape": [
+            8
+        ],
+        "low": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "low_repr": "[-90.        -90.         -5.         -5.         -3.1415927  -5.\n  -0.         -0.       ]",
+        "high_repr": "[90.        90.         5.         5.         3.1415927  5.\n  1.         1.       ]",
+        "_np_random": null
+    },
+    "action_space": {
+        ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=",
+        "n": "4",
+        "start": "0",
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 16,
+    "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,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1a48cfc5f88ff6866dd0f6bcfaa8be3b4ccd56756e16ae43fa083c686f86fd8d
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dfab346fea7471dc9ec48e8409de72d69faa9a2a7f6ecdd1b197a38b7d577df6
+size 43762

ppo-LunarLander-v2/pytorch_variables.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
+size 864

ppo-LunarLander-v2/system_info.txt

@@ -0,0 +1,9 @@
+- OS: Linux-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Tue Jun 18 14:18:04 UTC 2024
+- Python: 3.10.12
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.3.0+cu121
+- GPU Enabled: True
+- Numpy: 1.25.2
+- Cloudpickle: 2.2.1
+- Gymnasium: 0.28.1
+- OpenAI Gym: 0.25.2

results.json

@@ -0,0 +1 @@
+{"mean_reward": 241.9155432, "std_reward": 49.59177329040598, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-07-01T11:17:53.825242"}