testing the course

README.md

@@ -0,0 +1,37 @@
+---
+library_name: stable-baselines3
+tags:
+- LunarLander-v2
+- deep-reinforcement-learning
+- reinforcement-learning
+- stable-baselines3
+model-index:
+- name: MLPPolicy
+  results:
+  - task:
+      type: reinforcement-learning
+      name: reinforcement-learning
+    dataset:
+      name: LunarLander-v2
+      type: LunarLander-v2
+    metrics:
+    - type: mean_reward
+      value: 232.34 +/- 46.08
+      name: mean_reward
+      verified: false
+---
+
+# **MLPPolicy** Agent playing **LunarLander-v2**
+This is a trained model of a **MLPPolicy** 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 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 0x7f65cdef1ca0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f65cdef1d30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f65cdef1dc0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f65cdef1e50>", "_build": "<function ActorCriticPolicy._build at 0x7f65cdef1ee0>", "forward": "<function ActorCriticPolicy.forward at 0x7f65cdef1f70>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f65cdef7040>", "_predict": "<function ActorCriticPolicy._predict at 0x7f65cdef70d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f65cdef7160>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f65cdef71f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f65cdef7280>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f65cdf6e420>"}, "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": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1671824265202650883, "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": 248, "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, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}

ppo-lunar-test.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:332a3f709433c97c9559bdee97b21dace75e487671d65637fba4bef7ff896c13
+size 147218

ppo-lunar-test/_stable_baselines3_version

@@ -0,0 +1 @@
+1.6.2

ppo-lunar-test/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 0x7f65cdef1ca0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f65cdef1d30>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f65cdef1dc0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f65cdef1e50>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f65cdef1ee0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f65cdef1f70>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f65cdef7040>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f65cdef70d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f65cdef7160>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f65cdef71f0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f65cdef7280>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f65cdf6e420>"
+    },
+    "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": 1015808,
+    "_total_timesteps": 1000000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1671824265202650883,
+    "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": 248,
+    "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
+}

ppo-lunar-test/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:121f1f52b11eb13e2c43701e936ddf64ab10e560edbf193ecb93a028b3736511
+size 87929

ppo-lunar-test/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6a58672a92677050c3d77eb9d32001613d6ea5da6dc5ddf55be280cce7ce01f6
+size 43201

ppo-lunar-test/pytorch_variables.pth

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

ppo-lunar-test/system_info.txt

@@ -0,0 +1,7 @@
+OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
+Python: 3.8.16
+Stable-Baselines3: 1.6.2
+PyTorch: 1.13.0+cu116
+GPU Enabled: True
+Numpy: 1.21.6
+Gym: 0.21.0

results.json

@@ -0,0 +1 @@
+{"mean_reward": 232.33607343301597, "std_reward": 46.08023171010049, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-23T20:00:10.777665"}