Huggbottle commited on
Commit
ecccf0b
·
verified ·
1 Parent(s): 16e9e44

Upload of PPO DeepRL LinarLander Trained

Browse files
.gitattributes CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
 
 
33
  *.zip filter=lfs diff=lfs merge=lfs -text
34
  *.zst filter=lfs diff=lfs merge=lfs -text
35
  *tfevents* filter=lfs diff=lfs merge=lfs -text
36
+ replay.mp4 filter=lfs diff=lfs merge=lfs -text
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: 246.48 +/- 17.52
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 0x7868f32fdbc0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7868f32fdc60>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7868f32fdd00>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7868f32fdda0>", "_build": "<function ActorCriticPolicy._build at 0x7868f32fde40>", "forward": "<function ActorCriticPolicy.forward at 0x7868f32fdee0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7868f32fdf80>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7868f32fe020>", "_predict": "<function ActorCriticPolicy._predict at 0x7868f32fe0c0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7868f32fe160>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7868f32fe200>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7868f32fe2a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7868f323b300>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1000448, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1751035818472904808, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlgAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAABm0Km8rpHRPTI7vj1lOoa+jXbzOwa3pbwAAAAAAAAAAJSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJLAUsIhpSMAUOUdJRSlC4="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWAQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksBhZSMAUOUdJRSlC4="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.00044800000000000395, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 3908, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True True True True True True True True]", "bounded_above": "[ True True True True True True True True]", "_shape": [8], "low": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]", "high": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]", "low_repr": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]", "high_repr": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV3AAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFm51bXB5Ll9jb3JlLm11bHRpYXJyYXmUjAZzY2FsYXKUk5SMBW51bXB5lIwFZHR5cGWUk5SMAmk4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJDCAQAAAAAAAAAlIaUUpSMBXN0YXJ0lGgIaA5DCAAAAAAAAAAAlIaUUpSMBl9zaGFwZZQpjAVkdHlwZZRoDowKX25wX3JhbmRvbZROdWIu", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 1, "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, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-6.1.123+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Mar 30 16:01:29 UTC 2025", "Python": "3.11.13", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.6.0+cu124", "GPU Enabled": "True", "Numpy": "2.0.2", "Cloudpickle": "3.1.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}
ppo-LunarLander-v2.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3a5aafbada532f46aaa958c6ab60171143a48833df1626286a2a22c3b67097f9
3
+ size 147474
ppo-LunarLander-v2/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 2.0.0a5
ppo-LunarLander-v2/data ADDED
@@ -0,0 +1,99 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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 0x7868f32fdbc0>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7868f32fdc60>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7868f32fdd00>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7868f32fdda0>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7868f32fde40>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7868f32fdee0>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7868f32fdf80>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7868f32fe020>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7868f32fe0c0>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7868f32fe160>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7868f32fe200>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7868f32fe2a0>",
19
+ "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc._abc_data object at 0x7868f323b300>"
21
+ },
22
+ "verbose": 1,
23
+ "policy_kwargs": {},
24
+ "num_timesteps": 1000448,
25
+ "_total_timesteps": 1000000,
26
+ "_num_timesteps_at_start": 0,
27
+ "seed": null,
28
+ "action_noise": null,
29
+ "start_time": 1751035818472904808,
30
+ "learning_rate": 0.0003,
31
+ "tensorboard_log": null,
32
+ "_last_obs": {
33
+ ":type:": "<class 'numpy.ndarray'>",
34
+ ":serialized:": "gAWVlgAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAABm0Km8rpHRPTI7vj1lOoa+jXbzOwa3pbwAAAAAAAAAAJSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJLAUsIhpSMAUOUdJRSlC4="
35
+ },
36
+ "_last_episode_starts": {
37
+ ":type:": "<class 'numpy.ndarray'>",
38
+ ":serialized:": "gAWVdQAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWAQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksBhZSMAUOUdJRSlC4="
39
+ },
40
+ "_last_original_obs": null,
41
+ "_episode_num": 0,
42
+ "use_sde": false,
43
+ "sde_sample_freq": -1,
44
+ "_current_progress_remaining": -0.00044800000000000395,
45
+ "_stats_window_size": 100,
46
+ "ep_info_buffer": {
47
+ ":type:": "<class 'collections.deque'>",
48
+ ":serialized:": "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"
49
+ },
50
+ "ep_success_buffer": {
51
+ ":type:": "<class 'collections.deque'>",
52
+ ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
53
+ },
54
+ "_n_updates": 3908,
55
+ "observation_space": {
56
+ ":type:": "<class 'gymnasium.spaces.box.Box'>",
57
+ ":serialized:": "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",
58
+ "dtype": "float32",
59
+ "bounded_below": "[ True True True True True True True True]",
60
+ "bounded_above": "[ True True True True True True True True]",
61
+ "_shape": [
62
+ 8
63
+ ],
64
+ "low": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]",
65
+ "high": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]",
66
+ "low_repr": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]",
67
+ "high_repr": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]",
68
+ "_np_random": null
69
+ },
70
+ "action_space": {
71
+ ":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
72
+ ":serialized:": "gAWV3AAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFm51bXB5Ll9jb3JlLm11bHRpYXJyYXmUjAZzY2FsYXKUk5SMBW51bXB5lIwFZHR5cGWUk5SMAmk4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJDCAQAAAAAAAAAlIaUUpSMBXN0YXJ0lGgIaA5DCAAAAAAAAAAAlIaUUpSMBl9zaGFwZZQpjAVkdHlwZZRoDowKX25wX3JhbmRvbZROdWIu",
73
+ "n": "4",
74
+ "start": "0",
75
+ "_shape": [],
76
+ "dtype": "int64",
77
+ "_np_random": null
78
+ },
79
+ "n_envs": 1,
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:": "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"
91
+ },
92
+ "clip_range_vf": null,
93
+ "normalize_advantage": true,
94
+ "target_kl": null,
95
+ "lr_schedule": {
96
+ ":type:": "<class 'function'>",
97
+ ":serialized:": "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"
98
+ }
99
+ }
ppo-LunarLander-v2/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:86498e4dd123bf973685b71d01d66f5c0661dfc0601710e272afc8f2e59eb909
3
+ size 88362
ppo-LunarLander-v2/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:58eb296d6e559d9c5f1350f03744ea296cffa42715e78c10e125740ee7b5bec5
3
+ size 43762
ppo-LunarLander-v2/pytorch_variables.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
3
+ size 864
ppo-LunarLander-v2/system_info.txt ADDED
@@ -0,0 +1,9 @@
 
 
 
 
 
 
 
 
 
 
1
+ - OS: Linux-6.1.123+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Mar 30 16:01:29 UTC 2025
2
+ - Python: 3.11.13
3
+ - Stable-Baselines3: 2.0.0a5
4
+ - PyTorch: 2.6.0+cu124
5
+ - GPU Enabled: True
6
+ - Numpy: 2.0.2
7
+ - Cloudpickle: 3.1.1
8
+ - Gymnasium: 0.28.1
9
+ - OpenAI Gym: 0.25.2
replay.mp4 ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:5548df9a58eecb7ec78eb7cf81e54fa2d5062f89c1d9f33705ac763d01467f7c
3
+ size 141595
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 246.47930460000003, "std_reward": 17.517392449904733, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-06-27T15:39:25.781618"}