Upload PPO Chandrayaan-Lander trained agent

.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

Chandrayaan_lander.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1eb19d807fe4dc359763f9ba31145ffe401c17e4cb2949bdc6f5f54b35864bf9
+size 144147

Chandrayaan_lander/_stable_baselines3_version

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

Chandrayaan_lander/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 0x7fb1777d5cb0>",
+        "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb1777d5d40>",
+        "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fb1777d5dd0>",
+        "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fb1777d5e60>",
+        "_build": "<function ActorCriticPolicy._build at 0x7fb1777d5ef0>",
+        "forward": "<function ActorCriticPolicy.forward at 0x7fb1777d5f80>",
+        "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fb1777da050>",
+        "_predict": "<function ActorCriticPolicy._predict at 0x7fb1777da0e0>",
+        "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fb1777da170>",
+        "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fb1777da200>",
+        "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fb1777da290>",
+        "__abstractmethods__": "frozenset()",
+        "_abc_impl": "<_abc_data object at 0x7fb1777a38d0>"
+    },
+    "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": 507904,
+    "_total_timesteps": 500000,
+    "_num_timesteps_at_start": 0,
+    "seed": null,
+    "action_noise": null,
+    "start_time": 1655286805.423365,
+    "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": -0.015808000000000044,
+    "ep_info_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "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"
+    },
+    "ep_success_buffer": {
+        ":type:": "<class 'collections.deque'>",
+        ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
+    },
+    "_n_updates": 124,
+    "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
+}

Chandrayaan_lander/policy.optimizer.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8d05e20ba4336833b3ff3a4824c2e27205e554ef35c2a8271b89bc59e0cad4c0
+size 84829

Chandrayaan_lander/policy.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c145fa5f82a13797368883a60663c7fb29da2e1e2ae1069b787ce6da9cb6d19a
+size 43201

Chandrayaan_lander/pytorch_variables.pth

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

Chandrayaan_lander/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: 176.75 +/- 18.07
+      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 0x7fb1777d5cb0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fb1777d5d40>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fb1777d5dd0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fb1777d5e60>", "_build": "<function ActorCriticPolicy._build at 0x7fb1777d5ef0>", "forward": "<function ActorCriticPolicy.forward at 0x7fb1777d5f80>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fb1777da050>", "_predict": "<function ActorCriticPolicy._predict at 0x7fb1777da0e0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fb1777da170>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fb1777da200>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fb1777da290>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fb1777a38d0>"}, "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": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1655286805.423365, "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": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gASVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 124, "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-5.4.188+-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 sha256:9f130ff26879ba112184038d01f7bf50c123121d7a2a42e30ec3e25b76219fbc
+size 249692

results.json

@@ -0,0 +1 @@
+{"mean_reward": 176.75146271039299, "std_reward": 18.065682444016637, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-06-15T10:16:23.452611"}