My First PPO MlpPolicy model
Browse files- README.md +37 -0
- config.json +1 -0
- ppo-LunarLander-v2.zip +3 -0
- ppo-LunarLander-v2/_stable_baselines3_version +1 -0
- ppo-LunarLander-v2/data +95 -0
- ppo-LunarLander-v2/policy.optimizer.pth +3 -0
- ppo-LunarLander-v2/policy.pth +3 -0
- ppo-LunarLander-v2/pytorch_variables.pth +3 -0
- ppo-LunarLander-v2/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 MlpPolicy
|
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: 239.87 +/- 22.19
|
20 |
+
name: mean_reward
|
21 |
+
verified: false
|
22 |
+
---
|
23 |
+
|
24 |
+
# **PPO MlpPolicy** Agent playing **LunarLander-v2**
|
25 |
+
This is a trained model of a **PPO MlpPolicy** 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 0x7f30cd2185e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f30cd218670>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f30cd218700>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f30cd218790>", "_build": "<function ActorCriticPolicy._build at 0x7f30cd218820>", "forward": "<function ActorCriticPolicy.forward at 0x7f30cd2188b0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f30cd218940>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f30cd2189d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f30cd218a60>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f30cd218af0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f30cd218b80>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f30cd218c10>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f30cd2198c0>"}, "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": 1678303183267269277, "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": 310, "n_steps": 2048, "gamma": 0.99, "gae_lambda": 0.95, "ent_coef": 0.0, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 64, "n_epochs": 10, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022", "Python": "3.9.16", "Stable-Baselines3": "1.7.0", "PyTorch": "1.13.1+cu116", "GPU Enabled": "True", "Numpy": "1.22.4", "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:ea6dcdc92f93170b291c1c73cbfa9b4990dc1ad28ad7f63d457986b954cf095d
|
3 |
+
size 147412
|
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 0x7f30cd2185e0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f30cd218670>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f30cd218700>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f30cd218790>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f30cd218820>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f30cd2188b0>",
|
13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7f30cd218940>",
|
14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f30cd2189d0>",
|
15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f30cd218a60>",
|
16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f30cd218af0>",
|
17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f30cd218b80>",
|
18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f30cd218c10>",
|
19 |
+
"__abstractmethods__": "frozenset()",
|
20 |
+
"_abc_impl": "<_abc._abc_data object at 0x7f30cd2198c0>"
|
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": 1015808,
|
47 |
+
"_total_timesteps": 1000000,
|
48 |
+
"_num_timesteps_at_start": 0,
|
49 |
+
"seed": null,
|
50 |
+
"action_noise": null,
|
51 |
+
"start_time": 1678303183267269277,
|
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": 310,
|
80 |
+
"n_steps": 2048,
|
81 |
+
"gamma": 0.99,
|
82 |
+
"gae_lambda": 0.95,
|
83 |
+
"ent_coef": 0.0,
|
84 |
+
"vf_coef": 0.5,
|
85 |
+
"max_grad_norm": 0.5,
|
86 |
+
"batch_size": 64,
|
87 |
+
"n_epochs": 10,
|
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 sha256:56bd31a06f80484d17021b02c7e7bb29bdc5b51de81b57a071926e595c1979f1
|
3 |
+
size 87929
|
ppo-LunarLander-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ac1a9af7d3c8737a5f5a15e6e0261b6b9f80556efd487ef0c032837ab9212588
|
3 |
+
size 43393
|
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-5.10.147+-x86_64-with-glibc2.31 # 1 SMP Sat Dec 10 16:00:40 UTC 2022
|
2 |
+
- Python: 3.9.16
|
3 |
+
- Stable-Baselines3: 1.7.0
|
4 |
+
- PyTorch: 1.13.1+cu116
|
5 |
+
- GPU Enabled: True
|
6 |
+
- Numpy: 1.22.4
|
7 |
+
- Gym: 0.21.0
|
replay.mp4
ADDED
Binary file (196 kB). View file
|
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 239.87307728651822, "std_reward": 22.190135992967907, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-03-08T19:50:37.436956"}
|