kongacute commited on
Commit
69b8999
·
1 Parent(s): 17e230f

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: 227.39 +/- 61.86
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 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 0x7fa52aad4a60>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa52aad4af0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa52aad4b80>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa52aad4c10>", "_build": "<function ActorCriticPolicy._build at 0x7fa52aad4ca0>", "forward": "<function ActorCriticPolicy.forward at 0x7fa52aad4d30>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fa52aad4dc0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa52aad4e50>", "_predict": "<function ActorCriticPolicy._predict at 0x7fa52aad4ee0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa52aad4f70>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa52aae2040>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa52aae20d0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fa52aadb810>"}, "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": 1000448, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1675241478348365079, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAOZqfD1ak4o/LXmJvHmOor78J0w9qteivAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": -0.00044800000000000395, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 3908, "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:b9ada86969ebc14f22646d1338aeeeeeafd9d0a0faef58fa3beb0871cb84e6bb
3
+ size 146746
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 0x7fa52aad4a60>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fa52aad4af0>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fa52aad4b80>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fa52aad4c10>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7fa52aad4ca0>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7fa52aad4d30>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fa52aad4dc0>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fa52aad4e50>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7fa52aad4ee0>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fa52aad4f70>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fa52aae2040>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fa52aae20d0>",
19
+ "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc_data object at 0x7fa52aadb810>"
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": 1,
46
+ "num_timesteps": 1000448,
47
+ "_total_timesteps": 1000000,
48
+ "_num_timesteps_at_start": 0,
49
+ "seed": null,
50
+ "action_noise": null,
51
+ "start_time": 1675241478348365079,
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:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAOZqfD1ak4o/LXmJvHmOor78J0w9qteivAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
61
+ },
62
+ "_last_episode_starts": {
63
+ ":type:": "<class 'numpy.ndarray'>",
64
+ ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
65
+ },
66
+ "_last_original_obs": null,
67
+ "_episode_num": 0,
68
+ "use_sde": false,
69
+ "sde_sample_freq": -1,
70
+ "_current_progress_remaining": -0.00044800000000000395,
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": 3908,
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 sha256:844e2b7ac264fb99a40ee7bafaeea4146f47ebcfc9b5858a77660c0dcc11ae0d
3
+ size 87929
ppo-LunarLander-v2/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:813a4db6dcafc27675b72689f4090b9b7108afd78a3433a7577da4e55cb9563a
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 (226 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 227.3867831943884, "std_reward": 61.86477835901061, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-01T09:49:12.202754"}