Chayo commited on
Commit
2e3061c
·
1 Parent(s): 1ed6d1c

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: 173.24 +/- 14.93
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 0x7f853e457b90>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f853e457c20>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f853e457cb0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f853e457d40>", "_build": "<function ActorCriticPolicy._build at 0x7f853e457dd0>", "forward": "<function ActorCriticPolicy.forward at 0x7f853e457e60>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f853e457ef0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f853e457f80>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f853e45e050>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f853e45e0e0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f853e45e170>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f853e4a7990>"}, "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": 1669177219838697358, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAEAfTL7uDd68Wt/ru8Opjbq5ZEQ+pZFWOwAAgD8AAIA/2tFGPoe5iT9TZdo9AT58vpD2pr3jp8c9AAAAAAAAAADNvaM8LOKYPPZKITxacWe+R/KbPTDUjrwAAAAAAAAAAJo6vry3eL4/leLkvUu/dL3j4W69zDK5vQAAAAAAAAAAs1WpPaJ5ED4W32m66m3WvRC08Dxas8U8AAAAAAAAAACA5dG9KQgzugMEVLoPqqI1SjifNuNtcDkAAIA/AACAP+bnNj60Bco+Ccg7PNzOKr7E8aU7c2AMPgAAAAAAAAAAGmnFPfYsSbpW1lI7CTFAOND40Drgt/+5AACAPwAAgD+aHrK9KcApulJZJztX7t425Ea3OvatPboAAIA/AACAP80dJ70f/c+5T9g6POEpIDbsPHK5Ki4eNQAAgD8AAIA/05tGvsNOZzsG2B88Q4J9ucHsEL2Y52I6AACAPwAAgD9Dnse+qYN+PWKkADsxKgu5lrfYvQ6sKLoAAIA/AACAPzMC4zyPbnq6144MO2EHJTiCBSa6cPPruAAAgD8AAIA/Wn+ZPTgR4ru+tTo8axuKPWb9YTxoveu7AACAPwAAgD966Ei+VHLivKBdxzmPVlw4+2pEPrtMB7kAAIA/AACAP5oj0D32aBO6jSMqu01ENDZfKIa7FMJEOgAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="}, "_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:": "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"}, "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-5.10.133+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.7.15", "Stable-Baselines3": "1.6.2", "PyTorch": "1.12.1+cu113", "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:d4b37b9efe75e9c707a122caa736f5ee7765a6f4fa4f0f9cfd5d1e6df14a382f
3
+ size 147150
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 0x7f853e457b90>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f853e457c20>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f853e457cb0>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f853e457d40>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f853e457dd0>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f853e457e60>",
13
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f853e457ef0>",
14
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f853e457f80>",
15
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f853e45e050>",
16
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f853e45e0e0>",
17
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f853e45e170>",
18
+ "__abstractmethods__": "frozenset()",
19
+ "_abc_impl": "<_abc_data object at 0x7f853e4a7990>"
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": 16,
45
+ "num_timesteps": 507904,
46
+ "_total_timesteps": 500000,
47
+ "_num_timesteps_at_start": 0,
48
+ "seed": null,
49
+ "action_noise": null,
50
+ "start_time": 1669177219838697358,
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:": "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"
60
+ },
61
+ "_last_episode_starts": {
62
+ ":type:": "<class 'numpy.ndarray'>",
63
+ ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
64
+ },
65
+ "_last_original_obs": null,
66
+ "_episode_num": 0,
67
+ "use_sde": false,
68
+ "sde_sample_freq": -1,
69
+ "_current_progress_remaining": -0.015808000000000044,
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": 124,
79
+ "n_steps": 1024,
80
+ "gamma": 0.999,
81
+ "gae_lambda": 0.98,
82
+ "ent_coef": 0.01,
83
+ "vf_coef": 0.5,
84
+ "max_grad_norm": 0.5,
85
+ "batch_size": 64,
86
+ "n_epochs": 4,
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:fe840ddfcacf558158113edf983b3c8b89ebddaa082d2667349b21234b19e8d8
3
+ size 87865
ppo-LunarLander-v2/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:1acb9acf1536a389c6fbc0b93d9af421bda9755c64a729f4ec92ecb04c8a968a
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-Ubuntu-18.04-bionic #1 SMP Fri Aug 26 08:44:51 UTC 2022
2
+ Python: 3.7.15
3
+ Stable-Baselines3: 1.6.2
4
+ PyTorch: 1.12.1+cu113
5
+ GPU Enabled: True
6
+ Numpy: 1.21.6
7
+ Gym: 0.21.0
replay.mp4 ADDED
Binary file (251 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 173.24375355931, "std_reward": 14.926364177955726, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-11-23T04:43:21.827739"}