João Neves commited on
Commit
29d5263
1 Parent(s): 228fb5e

First model

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: 255.97 +/- 18.46
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 0x7fd3332751f0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd333275280>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fd333275310>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fd3332753a0>", "_build": "<function ActorCriticPolicy._build at 0x7fd333275430>", "forward": "<function ActorCriticPolicy.forward at 0x7fd3332754c0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fd333275550>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fd3332755e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7fd333275670>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fd333275700>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fd333275790>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fd333275820>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7fd33326e7b0>"}, "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": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1675509262115018089, "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:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "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-5.15.0-58-generic-x86_64-with-glibc2.17 # 64~20.04.1-Ubuntu SMP Fri Jan 6 16:42:31 UTC 2023", "Python": "3.8.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1", "GPU Enabled": "False", "Numpy": "1.23.3", "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:c9aa67818e253d07ee8f9eca3e592dc3fa67c8de18e10d719895df823cbda544
3
+ size 147050
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 0x7fd3332751f0>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fd333275280>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fd333275310>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fd3332753a0>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7fd333275430>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7fd3332754c0>",
13
+ "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fd333275550>",
14
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fd3332755e0>",
15
+ "_predict": "<function ActorCriticPolicy._predict at 0x7fd333275670>",
16
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fd333275700>",
17
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fd333275790>",
18
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fd333275820>",
19
+ "__abstractmethods__": "frozenset()",
20
+ "_abc_impl": "<_abc_data object at 0x7fd33326e7b0>"
21
+ },
22
+ "verbose": 1,
23
+ "policy_kwargs": {},
24
+ "observation_space": {
25
+ ":type:": "<class 'gym.spaces.box.Box'>",
26
+ ":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAAAAAAAAAAAlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu",
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": 1015808,
47
+ "_total_timesteps": 1000000,
48
+ "_num_timesteps_at_start": 0,
49
+ "seed": null,
50
+ "action_noise": null,
51
+ "start_time": 1675509262115018089,
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": 248,
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
+ }
ppo-LunarLander-v2/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:6bb3a8da9ca9094b9f0ddaf2ce82d5f0a6d85aba2717597da75f1780c248744b
3
+ size 87545
ppo-LunarLander-v2/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:f1384d892104434c4eedf57786cba04fccf76c2967709034ad5a556f5e771e60
3
+ size 43265
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-5.15.0-58-generic-x86_64-with-glibc2.17 # 64~20.04.1-Ubuntu SMP Fri Jan 6 16:42:31 UTC 2023
2
+ - Python: 3.8.16
3
+ - Stable-Baselines3: 1.7.0
4
+ - PyTorch: 1.13.1
5
+ - GPU Enabled: False
6
+ - Numpy: 1.23.3
7
+ - Gym: 0.21.0
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 255.96729637772017, "std_reward": 18.460992483798027, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-02-04T11:51:04.191315"}