adlrocha commited on
Commit
3024231
·
1 Parent(s): 242f326

first lunar landing training

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: 253.96 +/- 15.08
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 0x7f4b9a46bca0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4b9a46bd30>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4b9a46bdc0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4b9a46be50>", "_build": "<function ActorCriticPolicy._build at 0x7f4b9a46bee0>", "forward": "<function ActorCriticPolicy.forward at 0x7f4b9a46bf70>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4b9a470040>", "_predict": "<function ActorCriticPolicy._predict at 0x7f4b9a4700d0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4b9a470160>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4b9a4701f0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4b9a470280>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f4b9a468420>"}, "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": 1671700040356880051, "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": 252, "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-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"}}
dqn_lunar.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:3a4d6236e83b5749ce9c66d8d5c8ce78b23d521de916539322058749b079e223
3
+ size 147210
dqn_lunar/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 1.6.2
dqn_lunar/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 0x7f4b9a46bca0>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4b9a46bd30>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4b9a46bdc0>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4b9a46be50>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f4b9a46bee0>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f4b9a46bf70>",
13
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4b9a470040>",
14
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f4b9a4700d0>",
15
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4b9a470160>",
16
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4b9a4701f0>",
17
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4b9a470280>",
18
+ "__abstractmethods__": "frozenset()",
19
+ "_abc_impl": "<_abc_data object at 0x7f4b9a468420>"
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": 1015808,
46
+ "_total_timesteps": 1000000,
47
+ "_num_timesteps_at_start": 0,
48
+ "seed": null,
49
+ "action_noise": null,
50
+ "start_time": 1671700040356880051,
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": 252,
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
+ }
dqn_lunar/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:c10fbea4cc2c2ada2cd51fc754882936525cdfc34a0a0cf85733aa0f5e46f392
3
+ size 87929
dqn_lunar/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:1a36e60adaca7da63ecd64921afb9362f9d35fa3311fe34eafd0796c40be245d
3
+ size 43201
dqn_lunar/pytorch_variables.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
3
+ size 431
dqn_lunar/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 (241 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 253.95741337417894, "std_reward": 15.075135227648135, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-22T09:53:24.887441"}