bguan commited on
Commit
d3f6efb
1 Parent(s): 7d46eab

1st commit of DeepRL course v2 unit1 lunar lander lab trained 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: 267.17 +/- 17.66
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 0x7f00419b6160>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f00419b61f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f00419b6280>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f00419b6310>", "_build": "<function ActorCriticPolicy._build at 0x7f00419b63a0>", "forward": "<function ActorCriticPolicy.forward at 0x7f00419b6430>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f00419b64c0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f00419b6550>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f00419b65e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f00419b6670>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f00419b6700>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f00419b15d0>"}, "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": 1007616, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1670372470685307427, "learning_rate": 0.0005, "tensorboard_log": "logs", "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": -0.007616000000000067, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 492, "n_steps": 512, "gamma": 0.995, "gae_lambda": 0.985, "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": "3.8.15", "Stable-Baselines3": "1.6.2", "PyTorch": "1.12.1+cu113", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
ppo_lunarlander_221206.zip ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:767d1c47350ca16f01b2f8e4715ac4a51d4014108895b0349eefbc2f2c993a23
3
+ size 147048
ppo_lunarlander_221206/_stable_baselines3_version ADDED
@@ -0,0 +1 @@
 
 
1
+ 1.6.2
ppo_lunarlander_221206/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 0x7f00419b6160>",
8
+ "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f00419b61f0>",
9
+ "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f00419b6280>",
10
+ "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f00419b6310>",
11
+ "_build": "<function ActorCriticPolicy._build at 0x7f00419b63a0>",
12
+ "forward": "<function ActorCriticPolicy.forward at 0x7f00419b6430>",
13
+ "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f00419b64c0>",
14
+ "_predict": "<function ActorCriticPolicy._predict at 0x7f00419b6550>",
15
+ "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f00419b65e0>",
16
+ "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f00419b6670>",
17
+ "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f00419b6700>",
18
+ "__abstractmethods__": "frozenset()",
19
+ "_abc_impl": "<_abc_data object at 0x7f00419b15d0>"
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": 1007616,
46
+ "_total_timesteps": 1000000,
47
+ "_num_timesteps_at_start": 0,
48
+ "seed": null,
49
+ "action_noise": null,
50
+ "start_time": 1670372470685307427,
51
+ "learning_rate": 0.0005,
52
+ "tensorboard_log": "logs",
53
+ "lr_schedule": {
54
+ ":type:": "<class 'function'>",
55
+ ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4BDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/QGJN0vGp/IWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
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": -0.007616000000000067,
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": 492,
79
+ "n_steps": 512,
80
+ "gamma": 0.995,
81
+ "gae_lambda": 0.985,
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_221206/policy.optimizer.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:24799f5f5c63e59bdadd931a78c28105cb0423ca7342ec3fe54dc660006e9fb1
3
+ size 87865
ppo_lunarlander_221206/policy.pth ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
2
+ oid sha256:50098dff722e04b88d51679c3270992210875b87280ace2401ecab9909b25174
3
+ size 43201
ppo_lunarlander_221206/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_221206/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: 3.8.15
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 (227 kB). View file
 
results.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"mean_reward": 267.1669799320259, "std_reward": 17.65611765188149, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-07T00:46:57.488638"}