eingrid commited on
Commit
97bc86c
·
1 Parent(s): 3c67956
README.md ADDED
@@ -0,0 +1,37 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ ---
2
+ library_name: stable-baselines3
3
+ tags:
4
+ - LunarLander-v2
5
+ - deep-reinforcement-learning
6
+ - reinforcement-learning
7
+ - stable-baselines3
8
+ model-index:
9
+ - name: PRO
10
+ results:
11
+ - task:
12
+ type: reinforcement-learning
13
+ name: reinforcement-learning
14
+ dataset:
15
+ name: LunarLander-v2
16
+ type: LunarLander-v2
17
+ metrics:
18
+ - type: mean_reward
19
+ value: 252.46 +/- 19.81
20
+ name: mean_reward
21
+ verified: false
22
+ ---
23
+
24
+ # **PRO** Agent playing **LunarLander-v2**
25
+ This is a trained model of a **PRO** agent playing **LunarLander-v2**
26
+ using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
27
+
28
+ ## Usage (with Stable-baselines3)
29
+ TODO: Add your code
30
+
31
+
32
+ ```python
33
+ from stable_baselines3 import ...
34
+ from huggingface_sb3 import load_from_hub
35
+
36
+ ...
37
+ ```
config.json ADDED
@@ -0,0 +1 @@
 
 
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 0x7fc667d7df70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc667d81040>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc667d810d0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc667d81160>", "_build": "<function ActorCriticPolicy._build at 0x7fc667d811f0>", "forward": "<function ActorCriticPolicy.forward at 0x7fc667d81280>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fc667d81310>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc667d813a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fc667d81430>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc667d814c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc667d81550>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc667d815e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fc667d79750>"}, "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": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1675009779534264854, "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:": "gAWVZhAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIsmg6Oxk+U0CUhpRSlIwBbJRLqYwBdJRHQHzRym/Firl1fZQoaAZoCWgPQwgmV7H4zb9iQJSGlFKUaBVN6ANoFkdAfN+siB5HE3V9lChoBmgJaA9DCJs6j4r/klBAlIaUUpRoFU3oA2gWR0B9BnDKoybhdX2UKGgGaAloD0MIUkSGVTy+YkCUhpRSlGgVTegDaBZHQH0MTundfsx1fZQoaAZoCWgPQwjPFDqvsQBfQJSGlFKUaBVN6ANoFkdAfRRLpRoAXHV9lChoBmgJaA9DCIId/wWCABRAlIaUUpRoFUvBaBZHQH0xtkBjnV51fZQoaAZoCWgPQwgpPj4hOxdgQJSGlFKUaBVN6ANoFkdAfUzKAavRq3V9lChoBmgJaA9DCFq3Qe23hEZAlIaUUpRoFUt/aBZHQH1PNtZV4ot1fZQoaAZoCWgPQwjuztptl8VlQJSGlFKUaBVN6ANoFkdAfVJlYEGJN3V9lChoBmgJaA9DCJWaPdAKZ2NAlIaUUpRoFU3oA2gWR0B9W8OTaCcxdX2UKGgGaAloD0MIxSCwcmilYUCUhpRSlGgVTegDaBZHQH1j6ePJaJR1fZQoaAZoCWgPQwjde7jkONViQJSGlFKUaBVN6ANoFkdAfWQk9U0el3V9lChoBmgJaA9DCCBDxw4qj19AlIaUUpRoFU3oA2gWR0B9af420iQldX2UKGgGaAloD0MI5/7qcV8NY0CUhpRSlGgVTegDaBZHQH1slkUbkwN1fZQoaAZoCWgPQwhv9DEfkDJmQJSGlFKUaBVN6ANoFkdAfb6DW9US7HV9lChoBmgJaA9DCNcxrrg4EVBAlIaUUpRoFUufaBZHQH3JhysCDEp1fZQoaAZoCWgPQwg8wJMWLlRlQJSGlFKUaBVN6ANoFkdAfcy8Djin53V9lChoBmgJaA9DCA9Iwr6d215AlIaUUpRoFU3oA2gWR0B9zpiUgSvldX2UKGgGaAloD0MIRRMoYhHVTECUhpRSlGgVS8xoFkdAfdFpnYg7o3V9lChoBmgJaA9DCNZSQNr/NEZAlIaUUpRoFUvFaBZHQH3fOyVv/BF1fZQoaAZoCWgPQwgPRuwTwB1kQJSGlFKUaBVN6ANoFkdAfeIxDst03nV9lChoBmgJaA9DCHCZ02UxSTZAlIaUUpRoFUuiaBZHQH3lAwXZXdV1fZQoaAZoCWgPQwhenznr0+1lQJSGlFKUaBVN6ANoFkdAfekjQAuIynV9lChoBmgJaA9DCJFigEQToEtAlIaUUpRoFUvBaBZHQH3yHfuTibV1fZQoaAZoCWgPQwhdGr/wStlbQJSGlFKUaBVN6ANoFkdAffRUDuBtlHV9lChoBmgJaA9DCA4yychZkkpAlIaUUpRoFUuvaBZHQH4IG606YE51fZQoaAZoCWgPQwhw0clS60RfQJSGlFKUaBVN6ANoFkdAfhp/EOy3TnV9lChoBmgJaA9DCJrS+luCbGNAlIaUUpRoFU3oA2gWR0B+Im3/giu/dX2UKGgGaAloD0MI7j7HR4sjKECUhpRSlGgVS6loFkdAfj+NbC79RHV9lChoBmgJaA9DCC9P54rS42ZAlIaUUpRoFU3oA2gWR0B+WsbPyCnQdX2UKGgGaAloD0MImPc40wRfZkCUhpRSlGgVTegDaBZHQH5dRYeT3Zh1fZQoaAZoCWgPQwikb9I0qJVkQJSGlFKUaBVN6ANoFkdAfmBx3FDOT3V9lChoBmgJaA9DCN0HILWJdGNAlIaUUpRoFU3oA2gWR0B+aikM1CPZdX2UKGgGaAloD0MIoRNCB92iYUCUhpRSlGgVTegDaBZHQH55PO2RaHN1fZQoaAZoCWgPQwh2ilWDsNJjQJSGlFKUaBVN6ANoFkdAfnxsdT5wfnV9lChoBmgJaA9DCPet1onLKTtAlIaUUpRoFUvCaBZHQH6DVS4vvjR1fZQoaAZoCWgPQwiel4qNeVpUQJSGlFKUaBVN6ANoFkdAfuFwDeTFEXV9lChoBmgJaA9DCL0Yyol2EWJAlIaUUpRoFU3oA2gWR0B+44Uh3aBadX2UKGgGaAloD0MIIVwBhXr8TUCUhpRSlGgVS6xoFkdAfunstkFwDXV9lChoBmgJaA9DCFJ8fEJ2EGNAlIaUUpRoFU3oA2gWR0B+9qbI91U3dX2UKGgGaAloD0MII028AzxZXkCUhpRSlGgVTegDaBZHQH76JSR8twt1fZQoaAZoCWgPQwilaVA0DxplQJSGlFKUaBVN6ANoFkdAfvz/20zCUHV9lChoBmgJaA9DCC1CsRU0L0xAlIaUUpRoFUu6aBZHQH8GxHLA57x1fZQoaAZoCWgPQwjkoISZtp8pQJSGlFKUaBVL0WgWR0B/CSGO+7DmdX2UKGgGaAloD0MIt0QuOIPaX0CUhpRSlGgVTegDaBZHQH8LFSKm8/V1fZQoaAZoCWgPQwi8dmnD4UBjQJSGlFKUaBVN6ANoFkdAfw1fywwCbXV9lChoBmgJaA9DCIEhq1u9XGBAlIaUUpRoFU3oA2gWR0B/H/qJMxoJdX2UKGgGaAloD0MIWwuz0M6NNkCUhpRSlGgVS6RoFkdAfyV6reZXuHV9lChoBmgJaA9DCCdmvRjK0mZAlIaUUpRoFU3oA2gWR0B/N8mJFb3XdX2UKGgGaAloD0MIKxN+qZ/tQ0CUhpRSlGgVS9BoFkdAf0g9rGipN3V9lChoBmgJaA9DCOMbCp+tZmhAlIaUUpRoFU3oA2gWR0B/Ur5mAbyZdX2UKGgGaAloD0MIIJp5ck36Z0CUhpRSlGgVTegDaBZHQH9tfhuO0b91fZQoaAZoCWgPQwi4zVSIR9BdQJSGlFKUaBVN6ANoFkdAf3Bv6j323HV9lChoBmgJaA9DCNwPeGAAGSZAlIaUUpRoFUvJaBZHQH9yr8zhxYJ1fZQoaAZoCWgPQwifOetTjl5qQJSGlFKUaBVNewJoFkdAf3gC+De0onV9lChoBmgJaA9DCBQEj2/vuF1AlIaUUpRoFU3oA2gWR0B/eRimVJL/dX2UKGgGaAloD0MIsmSO5V0CY0CUhpRSlGgVTegDaBZHQH+GUeMhouh1fZQoaAZoCWgPQwgj+Uogpa9jQJSGlFKUaBVN6ANoFkdAf490FKTSs3V9lChoBmgJaA9DCIv5uaGp3GRAlIaUUpRoFU3oA2gWR0B/9Iwh4dIYdX2UKGgGaAloD0MI1NUdi+1qZECUhpRSlGgVTegDaBZHQIAA14Z/CqJ1fZQoaAZoCWgPQwiPAG4Wr15jQJSGlFKUaBVN6ANoFkdAgAKXGn4wiHV9lChoBmgJaA9DCCLDKt7IK2dAlIaUUpRoFU3oA2gWR0CACe2gnMMadX2UKGgGaAloD0MI/P7Ni5PyZkCUhpRSlGgVTegDaBZHQIAMQVoHs1N1fZQoaAZoCWgPQwibV3VWCxxmQJSGlFKUaBVN6ANoFkdAgA127nPmgnV9lChoBmgJaA9DCPAyw0ZZDyNAlIaUUpRoFUvIaBZHQIAOqHXVbzN1fZQoaAZoCWgPQwhoXDgQkpdRQJSGlFKUaBVLyGgWR0CAFrZJ04ipdX2UKGgGaAloD0MImQzH85l8YECUhpRSlGgVTegDaBZHQIAbdZTyaux1fZQoaAZoCWgPQwi9p3LaU1pOQJSGlFKUaBVL02gWR0CAIZG6wt8NdX2UKGgGaAloD0MIoYDtYMSWM0CUhpRSlGgVS75oFkdAgCH9yksSTXV9lChoBmgJaA9DCMb9R6ZD6mRAlIaUUpRoFU3oA2gWR0CAJa1m8M/hdX2UKGgGaAloD0MImntI+N4FSUCUhpRSlGgVS6toFkdAgC2H6VMVUXV9lChoBmgJaA9DCB6Jl6dzL2VAlIaUUpRoFU3oA2gWR0CAM+wiaAnVdX2UKGgGaAloD0MIBU8hV2o1Y0CUhpRSlGgVTegDaBZHQIBCU+s5n151fZQoaAZoCWgPQwg3OBH92mxDQJSGlFKUaBVN6ANoFkdAgEPy0jTrmnV9lChoBmgJaA9DCJNWfENhs2FAlIaUUpRoFU3oA2gWR0CARSdbPhQ4dX2UKGgGaAloD0MI+nyUEReLZECUhpRSlGgVTegDaBZHQIBH67/XGwR1fZQoaAZoCWgPQwiGqphKP+NjQJSGlFKUaBVN6ANoFkdAgEhrEk0JnnV9lChoBmgJaA9DCI9srppn4GdAlIaUUpRoFU3oA2gWR0CAT2MYMvytdX2UKGgGaAloD0MIXwmkxC7iYkCUhpRSlGgVTegDaBZHQIBUlrRBu4x1fZQoaAZoCWgPQwjkafmBqxJFQJSGlFKUaBVLvWgWR0CAiMc+aBqcdX2UKGgGaAloD0MI6nqi68IRXUCUhpRSlGgVTegDaBZHQICQID5j6N51fZQoaAZoCWgPQwj0Fg/vOZBiQJSGlFKUaBVN6ANoFkdAgJpUngHeJ3V9lChoBmgJaA9DCF+VC5V/EUtAlIaUUpRoFUu5aBZHQICeCiTMaCN1fZQoaAZoCWgPQwhMwoU8AktgQJSGlFKUaBVN6ANoFkdAgJ4oddVvM3V9lChoBmgJaA9DCB6mfXN/XmNAlIaUUpRoFU3oA2gWR0CAp9uQZGaydX2UKGgGaAloD0MI3nU25B9qYUCUhpRSlGgVTegDaBZHQICzPY6GQCF1fZQoaAZoCWgPQwic24R7ZbllQJSGlFKUaBVN6ANoFkdAgLOqUVzp5nV9lChoBmgJaA9DCOxnsRTJHyLAlIaUUpRoFUvQaBZHQIC0MWoFV1h1fZQoaAZoCWgPQwgWhsjpa4NkQJSGlFKUaBVN6ANoFkdAgLbxE4Nqg3V9lChoBmgJaA9DCKTH7236ZzdAlIaUUpRoFU3oA2gWR0CAvh2r4nF6dX2UKGgGaAloD0MIoYUEjC4VXkCUhpRSlGgVTegDaBZHQIDDTD/EOy51fZQoaAZoCWgPQwhGYKxvYO5AQJSGlFKUaBVLv2gWR0CAxizSkTHsdX2UKGgGaAloD0MI1Lg3v+FoY0CUhpRSlGgVTegDaBZHQIDPVnuiN851fZQoaAZoCWgPQwh+rOC3IYpjQJSGlFKUaBVN6ANoFkdAgNCz9bX6InV9lChoBmgJaA9DCPtalxohOGRAlIaUUpRoFU3oA2gWR0CA0caqCHymdX2UKGgGaAloD0MIixh2GJNYYUCUhpRSlGgVTegDaBZHQIDUT1schkl1fZQoaAZoCWgPQwgKZ7eWSXRhQJSGlFKUaBVN6ANoFkdAgNS61LJ0XHV9lChoBmgJaA9DCCbl7nN81GtAlIaUUpRoFU0rAmgWR0CA1PgYxcmjdX2UKGgGaAloD0MIkrOwpx3MXUCUhpRSlGgVTegDaBZHQIDe2s5n14B1fZQoaAZoCWgPQwguVP61vMI3QJSGlFKUaBVLwWgWR0CA4NK4hEBsdWUu"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_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-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
ppo-LunarLander-v2.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:1c425a2b4d1817182ec1433da50f18b02526d6314a34f86847fa76672e0c80bc
3
+ size 147386
ppo-LunarLander-v2/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 1.7.0
ppo-LunarLander-v2/data ADDED
@@ -0,0 +1,95 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "policy_class": {
3
+ ":type:": "<class 'abc.ABCMeta'>",
4
+ ":serialized:": "gAWVOwAAAAAAAACMIXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5wb2xpY2llc5SMEUFjdG9yQ3JpdGljUG9saWN5lJOULg==",
5
+ "__module__": "stable_baselines3.common.policies",
6
+ "__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 ",
7
+ "__init__": "<function ActorCriticPolicy.__init__ at 0x7fc667d7df70>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fc667d81040>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fc667d810d0>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fc667d81160>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7fc667d811f0>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7fc667d81280>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fc667d81310>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fc667d813a0>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7fc667d81430>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fc667d814c0>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fc667d81550>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fc667d815e0>",
19
+ "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc_data object at 0x7fc667d79750>"
21
+ },
22
+ "verbose": 1,
23
+ "policy_kwargs": {},
24
+ "observation_space": {
25
+ ":type:": "<class 'gym.spaces.box.Box'>",
26
+ ":serialized:": "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",
27
+ "dtype": "float32",
28
+ "_shape": [
29
+ 8
30
+ ],
31
+ "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
32
+ "high": "[inf inf inf inf inf inf inf inf]",
33
+ "bounded_below": "[False False False False False False False False]",
34
+ "bounded_above": "[False False False False False False False False]",
35
+ "_np_random": null
36
+ },
37
+ "action_space": {
38
+ ":type:": "<class 'gym.spaces.discrete.Discrete'>",
39
+ ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
40
+ "n": 4,
41
+ "_shape": [],
42
+ "dtype": "int64",
43
+ "_np_random": null
44
+ },
45
+ "n_envs": 16,
46
+ "num_timesteps": 507904,
47
+ "_total_timesteps": 500000,
48
+ "_num_timesteps_at_start": 0,
49
+ "seed": null,
50
+ "action_noise": null,
51
+ "start_time": 1675009779534264854,
52
+ "learning_rate": 0.0003,
53
+ "tensorboard_log": null,
54
+ "lr_schedule": {
55
+ ":type:": "<class 'function'>",
56
+ ":serialized:": "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"
57
+ },
58
+ "_last_obs": {
59
+ ":type:": "<class 'numpy.ndarray'>",
60
+ ":serialized:": "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"
61
+ },
62
+ "_last_episode_starts": {
63
+ ":type:": "<class 'numpy.ndarray'>",
64
+ ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
65
+ },
66
+ "_last_original_obs": null,
67
+ "_episode_num": 0,
68
+ "use_sde": false,
69
+ "sde_sample_freq": -1,
70
+ "_current_progress_remaining": -0.015808000000000044,
71
+ "ep_info_buffer": {
72
+ ":type:": "<class 'collections.deque'>",
73
+ ":serialized:": "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"
74
+ },
75
+ "ep_success_buffer": {
76
+ ":type:": "<class 'collections.deque'>",
77
+ ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
78
+ },
79
+ "_n_updates": 124,
80
+ "n_steps": 1024,
81
+ "gamma": 0.999,
82
+ "gae_lambda": 0.98,
83
+ "ent_coef": 0.01,
84
+ "vf_coef": 0.5,
85
+ "max_grad_norm": 0.5,
86
+ "batch_size": 64,
87
+ "n_epochs": 4,
88
+ "clip_range": {
89
+ ":type:": "<class 'function'>",
90
+ ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
91
+ },
92
+ "clip_range_vf": null,
93
+ "normalize_advantage": true,
94
+ "target_kl": null
95
+ }
ppo-LunarLander-v2/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:bed9dfb0fa50ccd530372681718a2dba1fb119d992fc4612e056fb004cdab3d0
3
+ size 87929
ppo-LunarLander-v2/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:79c2394bcc8179623207f3d9302dfb53dfcaaecbc17540abf3770819b9ea565d
3
+ size 43393
ppo-LunarLander-v2/pytorch_variables.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
3
+ size 431
ppo-LunarLander-v2/system_info.txt ADDED
@@ -0,0 +1,7 @@
 
 
 
 
 
 
 
 
1
+ - OS: Linux-5.10.147+-x86_64-with-glibc2.29 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
2
+ - Python: 3.8.10
3
+ - Stable-Baselines3: 1.7.0
4
+ - PyTorch: 1.13.1+cu116
5
+ - GPU Enabled: True
6
+ - Numpy: 1.21.6
7
+ - Gym: 0.21.0
replay.mp4 ADDED
Binary file (239 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 252.45956099717696, "std_reward": 19.806247620063786, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-29T16:57:02.853894"}