Upload PPO LunarLander-v2 trained agent to HF hub
Browse files- README.md +37 -0
- config.json +1 -0
- ppo-LunarLander-v.zip +3 -0
- ppo-LunarLander-v/_stable_baselines3_version +1 -0
- ppo-LunarLander-v/data +95 -0
- ppo-LunarLander-v/policy.optimizer.pth +3 -0
- ppo-LunarLander-v/policy.pth +3 -0
- ppo-LunarLander-v/pytorch_variables.pth +3 -0
- ppo-LunarLander-v/system_info.txt +7 -0
- replay.mp4 +0 -0
- results.json +1 -0
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: 285.50 +/- 21.30
|
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 0x7faaaeb2f160>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7faaaeb2f1f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7faaaeb2f280>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7faaaeb2f310>", "_build": "<function ActorCriticPolicy._build at 0x7faaaeb2f3a0>", "forward": "<function ActorCriticPolicy.forward at 0x7faaaeb2f430>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7faaaeb2f4c0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7faaaeb2f550>", "_predict": "<function ActorCriticPolicy._predict at 0x7faaaeb2f5e0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7faaaeb2f670>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7faaaeb2f700>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7faaaeb2f790>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7faaaeb97cf0>"}, "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": 2015232, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1673596650436733111, "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": -0.007616000000000067, "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": 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:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
|
ppo-LunarLander-v.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:d2a1ad61301d0c3a8fd9ff2376f3fd909f01b2d8ad152e709ff4778fc203e834
|
3 |
+
size 147316
|
ppo-LunarLander-v/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
1.7.0
|
ppo-LunarLander-v/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 0x7faaaeb2f160>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7faaaeb2f1f0>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7faaaeb2f280>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7faaaeb2f310>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7faaaeb2f3a0>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7faaaeb2f430>",
|
13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7faaaeb2f4c0>",
|
14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7faaaeb2f550>",
|
15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7faaaeb2f5e0>",
|
16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7faaaeb2f670>",
|
17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7faaaeb2f700>",
|
18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7faaaeb2f790>",
|
19 |
+
"__abstractmethods__": "frozenset()",
|
20 |
+
"_abc_impl": "<_abc_data object at 0x7faaaeb97cf0>"
|
21 |
+
},
|
22 |
+
"verbose": 1,
|
23 |
+
"policy_kwargs": {},
|
24 |
+
"observation_space": {
|
25 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
26 |
+
":serialized:": "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",
|
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": 2015232,
|
47 |
+
"_total_timesteps": 2000000,
|
48 |
+
"_num_timesteps_at_start": 0,
|
49 |
+
"seed": null,
|
50 |
+
"action_noise": null,
|
51 |
+
"start_time": 1673596650436733111,
|
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": -0.007616000000000067,
|
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": 492,
|
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-v/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:a3bd22a65aefe455ca537a8053ed37c96e6128a8814e25b56c0bc8d8e20da11b
|
3 |
+
size 87929
|
ppo-LunarLander-v/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:fd406532f09d6991e78d21b77511972ece3392545e5e4cb0067af80a41c10e08
|
3 |
+
size 43393
|
ppo-LunarLander-v/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-v/system_info.txt
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
- OS: Linux-5.10.147+-x86_64-with-glibc2.27 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
|
2 |
+
- Python: 3.8.16
|
3 |
+
- Stable-Baselines3: 1.7.0
|
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 (193 kB). View file
|
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 285.5011114209018, "std_reward": 21.304540920284815, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-13T09:04:38.566892"}
|