Upload PPO LunarLander-v2 trained agent

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: -133.78 +/- 104.53
+      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 0x784ac88677e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x784ac8867880>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x784ac8867920>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x784ac88679c0>", "_build": "<function ActorCriticPolicy._build at 0x784ac8867a60>", "forward": "<function ActorCriticPolicy.forward at 0x784ac8867b00>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x784ac8867ba0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x784ac8867c40>", "_predict": "<function ActorCriticPolicy._predict at 0x784ac8867ce0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x784ac8867d80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x784ac8867e20>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x784ac8867ec0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x784ac886ca00>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 16384, "_total_timesteps": 100, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1729278949290530218, "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": -162.84, "_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": 4, "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": 1024, "gamma": 0.999, "gae_lambda": 0.98, "ent_coef": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 4, "clip_range": {":type:": "<class 'function'>", ":serialized:": "gAWVJgMAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwiVAZcAiQFTAJROhZQpjAFflIWUjHEvaG9tZS9jYXJsb3NhZ3VheW8vcmVwby91YnVudHUtbm90ZWJvb2tzLTIvZW52XzMxMS9saWIvcHl0aG9uMy4xMS9zaXRlLXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlEuEQwj4gADYDxKICpRDAJSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjHEvaG9tZS9jYXJsb3NhZ3VheW8vcmVwby91YnVudHUtbm90ZWJvb2tzLTIvZW52XzMxMS9saWIvcHl0aG9uMy4xMS9zaXRlLXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlGgAjBJfZnVuY3Rpb25fc2V0c3RhdGWUk5RoIX2UfZQoaBiMBGZ1bmOUjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoGYwHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-6.8.0-47-generic-x86_64-with-glibc2.39 # 47-Ubuntu SMP PREEMPT_DYNAMIC Fri Sep 27 21:40:26 UTC 2024", "Python": "3.11.10", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.4.0+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "3.1.0", "Gymnasium": "0.28.1"}}

ppo-LunarLander-v2.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:97142a37ab83d310f91a58d6f38905730980425fcdaa5568521730dec89b0928
+size 148190

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 0x784ac88677e0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x784ac8867880>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x784ac8867920>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x784ac88679c0>",
+        "_build": "<function ActorCriticPolicy._build at 0x784ac8867a60>",
+        "forward": "<function ActorCriticPolicy.forward at 0x784ac8867b00>",
+        "extract_features": "<function ActorCriticPolicy.extract_features at 0x784ac8867ba0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x784ac8867c40>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x784ac8867ce0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x784ac8867d80>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x784ac8867e20>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x784ac8867ec0>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc._abc_data object at 0x784ac886ca00>"
+    },
+    "verbose": 1,
+    "policy_kwargs": {},
+    "num_timesteps": 16384,
+    "_total_timesteps": 100,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1729278949290530218,
+    "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": -162.84,
+    "_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": 4,
+    "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": 1024,
+    "gamma": 0.999,
+    "gae_lambda": 0.98,
+    "ent_coef": 0.01,
+    "vf_coef": 0.5,
+    "max_grad_norm": 0.5,
+    "batch_size": 64,
+    "n_epochs": 4,
+    "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:": "gAWVJgMAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwiVAZcAiQFTAJROhZQpjAFflIWUjHEvaG9tZS9jYXJsb3NhZ3VheW8vcmVwby91YnVudHUtbm90ZWJvb2tzLTIvZW52XzMxMS9saWIvcHl0aG9uMy4xMS9zaXRlLXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlEuEQwj4gADYDxKICpRDAJSMA3ZhbJSFlCl0lFKUfZQojAtfX3BhY2thZ2VfX5SMGHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbpSMCF9fbmFtZV9flIwec3RhYmxlX2Jhc2VsaW5lczMuY29tbW9uLnV0aWxzlIwIX19maWxlX1+UjHEvaG9tZS9jYXJsb3NhZ3VheW8vcmVwby91YnVudHUtbm90ZWJvb2tzLTIvZW52XzMxMS9saWIvcHl0aG9uMy4xMS9zaXRlLXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlGgAjBJfZnVuY3Rpb25fc2V0c3RhdGWUk5RoIX2UfZQoaBiMBGZ1bmOUjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoGYwHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/M6kqMFUyYYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
+    }
+}

ppo-LunarLander-v2/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4801fb47829403002a97274a9957325ae5a9279794c847a508933d59c1ff6727
+size 88362

ppo-LunarLander-v2/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d2b7ff925787ba7d843e39943ced431a82578c6e619135d440a3739d2eaf9fa7
+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 +1,8 @@
+- OS: Linux-6.8.0-47-generic-x86_64-with-glibc2.39 # 47-Ubuntu SMP PREEMPT_DYNAMIC Fri Sep 27 21:40:26 UTC 2024
+- Python: 3.11.10
+- Stable-Baselines3: 2.0.0a5
+- PyTorch: 2.4.0+cu121
+- GPU Enabled: True
+- Numpy: 1.26.4
+- Cloudpickle: 3.1.0
+- Gymnasium: 0.28.1

results.json

@@ -0,0 +1 @@
+{"mean_reward": -133.78447432191462, "std_reward": 104.53316202744428, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-10-18T15:15:53.787255"}