first commit for course

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: -799.96 +/- 403.93
+      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 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 0x7f3c5984b5e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3c5984b670>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3c5984b700>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3c5984b790>", "_build": "<function ActorCriticPolicy._build at 0x7f3c5984b820>", "forward": "<function ActorCriticPolicy.forward at 0x7f3c5984b8b0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f3c5984b940>", "_predict": "<function ActorCriticPolicy._predict at 0x7f3c5984b9d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3c5984ba60>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3c5984baf0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3c5984bb80>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f3c59840ea0>"}, "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": "RandomState(MT19937)"}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 1, "num_timesteps": 0, "_total_timesteps": 0, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": null, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": null, "_last_episode_starts": null, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 1, "ep_info_buffer": null, "ep_success_buffer": null, "_n_updates": 0, "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-5.10.133+-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"}}

moonlander.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fd979d9df0e9d602ece445dd686e96f46b6599c3dc4eca714bf5f71be333d93c
+size 56475

moonlander/_stable_baselines3_version

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

moonlander/data

@@ -0,0 +1,82 @@
+{
+    "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 0x7f3c5984b5e0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f3c5984b670>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f3c5984b700>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f3c5984b790>",
+        "_build": "<function ActorCriticPolicy._build at 0x7f3c5984b820>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7f3c5984b8b0>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f3c5984b940>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7f3c5984b9d0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f3c5984ba60>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f3c5984baf0>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f3c5984bb80>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7f3c59840ea0>"
+    },
+    "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": "RandomState(MT19937)"
+    },
+    "action_space": {
+        ":type:": "<class 'gym.spaces.discrete.Discrete'>",
+        ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
+        "n": 4,
+        "_shape": [],
+        "dtype": "int64",
+        "_np_random": null
+    },
+    "n_envs": 1,
+    "num_timesteps": 0,
+    "_total_timesteps": 0,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": null,
+    "learning_rate": 0.0003,
+    "tensorboard_log": null,
+    "lr_schedule": {
+        ":type:": "<class 'function'>",
+        ":serialized:": "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"
+    },
+    "_last_obs": null,
+    "_last_episode_starts": null,
+    "_last_original_obs": null,
+    "_episode_num": 0,
+    "use_sde": false,
+    "sde_sample_freq": -1,
+    "_current_progress_remaining": 1,
+    "ep_info_buffer": null,
+    "ep_success_buffer": null,
+    "_n_updates": 0,
+    "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:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4BDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
+    },
+    "clip_range_vf": null,
+    "normalize_advantage": true,
+    "target_kl": null
+}

moonlander/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2497affac19a461e040f7a57c9a5933e93b10b5579b0a3d91d7d3978070520ec
+size 687

moonlander/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b35a781884408d0bc76bc47c1a398387a4dea46d4ff77a9a82a0920650e19cd5
+size 43201

moonlander/pytorch_variables.pth

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

moonlander/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": -799.9607994821854, "std_reward": 403.92539942404676, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-12T04:15:38.077857"}