ChillerAlpha commited on
Commit
14efcf5
1 Parent(s): 92955d6

Upload PPO LunarLander-v2 trained agent

Browse files
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: PPO
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: 190.04 +/- 65.99
20
+ name: mean_reward
21
+ verified: false
22
+ ---
23
+
24
+ # **PPO** Agent playing **LunarLander-v2**
25
+ This is a trained model of a **PPO** 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 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 0x7fcba14fe670>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcba14fe700>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcba14fe790>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcba14fe820>", "_build": "<function ActorCriticPolicy._build at 0x7fcba14fe8b0>", "forward": "<function ActorCriticPolicy.forward at 0x7fcba14fe940>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcba14fe9d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fcba14fea60>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcba14feaf0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcba14feb80>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcba14fec10>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fcba14f5de0>"}, "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": 1, "num_timesteps": 200704, "_total_timesteps": 200000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1670581143301426623, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4BDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/M6kqMFUyYYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAA0eGj4fmK4+ostwvBbwIL6/86Y5+0XLuwAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.0035199999999999676, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 980, "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"}}
ppo-LunarLander-v2.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:84f183e7836d25c0c38a519b02ef231c44e9ab6b10bbee21993531b541354187
3
+ size 146547
ppo-LunarLander-v2/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 1.6.2
ppo-LunarLander-v2/data ADDED
@@ -0,0 +1,94 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 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 ",
7
+ "__init__": "<function ActorCriticPolicy.__init__ at 0x7fcba14fe670>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fcba14fe700>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fcba14fe790>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fcba14fe820>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7fcba14fe8b0>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7fcba14fe940>",
13
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fcba14fe9d0>",
14
+ "_predict": "<function ActorCriticPolicy._predict at 0x7fcba14fea60>",
15
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fcba14feaf0>",
16
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fcba14feb80>",
17
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fcba14fec10>",
18
+ "__abstractmethods__": "frozenset()",
19
+ "_abc_impl": "<_abc_data object at 0x7fcba14f5de0>"
20
+ },
21
+ "verbose": 1,
22
+ "policy_kwargs": {},
23
+ "observation_space": {
24
+ ":type:": "<class 'gym.spaces.box.Box'>",
25
+ ":serialized:": "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",
26
+ "dtype": "float32",
27
+ "_shape": [
28
+ 8
29
+ ],
30
+ "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
31
+ "high": "[inf inf inf inf inf inf inf inf]",
32
+ "bounded_below": "[False False False False False False False False]",
33
+ "bounded_above": "[False False False False False False False False]",
34
+ "_np_random": null
35
+ },
36
+ "action_space": {
37
+ ":type:": "<class 'gym.spaces.discrete.Discrete'>",
38
+ ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
39
+ "n": 4,
40
+ "_shape": [],
41
+ "dtype": "int64",
42
+ "_np_random": null
43
+ },
44
+ "n_envs": 1,
45
+ "num_timesteps": 200704,
46
+ "_total_timesteps": 200000,
47
+ "_num_timesteps_at_start": 0,
48
+ "seed": null,
49
+ "action_noise": null,
50
+ "start_time": 1670581143301426623,
51
+ "learning_rate": 0.0003,
52
+ "tensorboard_log": null,
53
+ "lr_schedule": {
54
+ ":type:": "<class 'function'>",
55
+ ":serialized:": "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"
56
+ },
57
+ "_last_obs": {
58
+ ":type:": "<class 'numpy.ndarray'>",
59
+ ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAA0eGj4fmK4+ostwvBbwIL6/86Y5+0XLuwAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
60
+ },
61
+ "_last_episode_starts": {
62
+ ":type:": "<class 'numpy.ndarray'>",
63
+ ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
64
+ },
65
+ "_last_original_obs": null,
66
+ "_episode_num": 0,
67
+ "use_sde": false,
68
+ "sde_sample_freq": -1,
69
+ "_current_progress_remaining": -0.0035199999999999676,
70
+ "ep_info_buffer": {
71
+ ":type:": "<class 'collections.deque'>",
72
+ ":serialized:": "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"
73
+ },
74
+ "ep_success_buffer": {
75
+ ":type:": "<class 'collections.deque'>",
76
+ ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
77
+ },
78
+ "_n_updates": 980,
79
+ "n_steps": 2048,
80
+ "gamma": 0.99,
81
+ "gae_lambda": 0.95,
82
+ "ent_coef": 0.0,
83
+ "vf_coef": 0.5,
84
+ "max_grad_norm": 0.5,
85
+ "batch_size": 64,
86
+ "n_epochs": 10,
87
+ "clip_range": {
88
+ ":type:": "<class 'function'>",
89
+ ":serialized:": "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"
90
+ },
91
+ "clip_range_vf": null,
92
+ "normalize_advantage": true,
93
+ "target_kl": null
94
+ }
ppo-LunarLander-v2/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:8ca4cf511304b0211858f99ccfa7f39750af0118855957c07f7c1ca4550a0763
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:f7c43f0da7703031a060b43ab21ff6128ddeaeffd238f5a27b94478bba23b9d5
3
+ size 43201
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-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
2
+ Python: 3.8.16
3
+ Stable-Baselines3: 1.6.2
4
+ PyTorch: 1.13.0+cu116
5
+ GPU Enabled: True
6
+ Numpy: 1.21.6
7
+ Gym: 0.21.0
replay.mp4 ADDED
Binary file (258 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 190.043618529938, "std_reward": 65.988380680364, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-09T10:32:08.545328"}