infinitejoy
commited on
Commit
•
a21ed35
1
Parent(s):
c266446
Upload PPO MountainCar-v0 trained agent
Browse files- .gitattributes +1 -0
- README.md +36 -0
- config.json +1 -0
- ppo-MountainCar-MlpPolicy-v2.zip +3 -0
- ppo-MountainCar-MlpPolicy-v2/_stable_baselines3_version +1 -0
- ppo-MountainCar-MlpPolicy-v2/data +94 -0
- ppo-MountainCar-MlpPolicy-v2/policy.optimizer.pth +3 -0
- ppo-MountainCar-MlpPolicy-v2/policy.pth +3 -0
- ppo-MountainCar-MlpPolicy-v2/pytorch_variables.pth +3 -0
- ppo-MountainCar-MlpPolicy-v2/system_info.txt +7 -0
- replay.mp4 +3 -0
- results.json +1 -0
.gitattributes
CHANGED
@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
25 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
26 |
*.zstandard filter=lfs diff=lfs merge=lfs -text
|
27 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
|
|
|
25 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
26 |
*.zstandard filter=lfs diff=lfs merge=lfs -text
|
27 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
28 |
+
*.mp4 filter=lfs diff=lfs merge=lfs -text
|
README.md
ADDED
@@ -0,0 +1,36 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
library_name: stable-baselines3
|
3 |
+
tags:
|
4 |
+
- MountainCar-v0
|
5 |
+
- deep-reinforcement-learning
|
6 |
+
- reinforcement-learning
|
7 |
+
- stable-baselines3
|
8 |
+
model-index:
|
9 |
+
- name: PPO
|
10 |
+
results:
|
11 |
+
- metrics:
|
12 |
+
- type: mean_reward
|
13 |
+
value: -116.10 +/- 13.68
|
14 |
+
name: mean_reward
|
15 |
+
task:
|
16 |
+
type: reinforcement-learning
|
17 |
+
name: reinforcement-learning
|
18 |
+
dataset:
|
19 |
+
name: MountainCar-v0
|
20 |
+
type: MountainCar-v0
|
21 |
+
---
|
22 |
+
|
23 |
+
# **PPO** Agent playing **MountainCar-v0**
|
24 |
+
This is a trained model of a **PPO** agent playing **MountainCar-v0**
|
25 |
+
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
|
26 |
+
|
27 |
+
## Usage (with Stable-baselines3)
|
28 |
+
TODO: Add your code
|
29 |
+
|
30 |
+
|
31 |
+
```python
|
32 |
+
from stable_baselines3 import ...
|
33 |
+
from huggingface_sb3 import load_from_hub
|
34 |
+
|
35 |
+
...
|
36 |
+
```
|
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 0x7f8d66277c10>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8d66277ca0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8d66277d30>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8d66277dc0>", "_build": "<function ActorCriticPolicy._build at 0x7f8d66277e50>", "forward": "<function ActorCriticPolicy.forward at 0x7f8d66277ee0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f8d66277f70>", "_predict": "<function ActorCriticPolicy._predict at 0x7f8d6627b040>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f8d6627b0d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f8d6627b160>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f8d6627b1f0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f8d66264c30>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True True]", "bounded_above": "[ True True]", "_shape": [2], "low": "[-1.2 -0.07]", "high": "[0.6 0.07]", "low_repr": "[-1.2 -0.07]", "high_repr": "[0.6 0.07]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVjAAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLA4wFc3RhcnSUSwCMBl9zaGFwZZQpjAVkdHlwZZSMBW51bXB5lGgIk5SMAmk4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMCl9ucF9yYW5kb22UTnViLg==", "n": 3, "start": 0, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 1, "num_timesteps": 5000192, "_total_timesteps": 5000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1656909885.5959098, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": null, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -3.8399999999993994e-05, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 19532, "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": "macOS-10.15.7-x86_64-i386-64bit Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64", "Python": "3.8.5", "Stable-Baselines3": "1.5.0", "PyTorch": "1.12.0", "GPU Enabled": "False", "Numpy": "1.23.0", "Gym": "0.24.1"}}
|
ppo-MountainCar-MlpPolicy-v2.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:2c67dc09b7c1774cd1f84f18a033edd0d4049809489949a6a2a545c0fec5cb54
|
3 |
+
size 134121
|
ppo-MountainCar-MlpPolicy-v2/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
1.5.0
|
ppo-MountainCar-MlpPolicy-v2/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 0x7f8d66277c10>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8d66277ca0>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8d66277d30>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8d66277dc0>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f8d66277e50>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f8d66277ee0>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f8d66277f70>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f8d6627b040>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f8d6627b0d0>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f8d6627b160>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f8d6627b1f0>",
|
18 |
+
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc_data object at 0x7f8d66264c30>"
|
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 |
+
"bounded_below": "[ True True]",
|
28 |
+
"bounded_above": "[ True True]",
|
29 |
+
"_shape": [
|
30 |
+
2
|
31 |
+
],
|
32 |
+
"low": "[-1.2 -0.07]",
|
33 |
+
"high": "[0.6 0.07]",
|
34 |
+
"low_repr": "[-1.2 -0.07]",
|
35 |
+
"high_repr": "[0.6 0.07]",
|
36 |
+
"_np_random": null
|
37 |
+
},
|
38 |
+
"action_space": {
|
39 |
+
":type:": "<class 'gym.spaces.discrete.Discrete'>",
|
40 |
+
":serialized:": "gAWVjAAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLA4wFc3RhcnSUSwCMBl9zaGFwZZQpjAVkdHlwZZSMBW51bXB5lGgIk5SMAmk4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMCl9ucF9yYW5kb22UTnViLg==",
|
41 |
+
"n": 3,
|
42 |
+
"start": 0,
|
43 |
+
"_shape": [],
|
44 |
+
"dtype": "int64",
|
45 |
+
"_np_random": null
|
46 |
+
},
|
47 |
+
"n_envs": 1,
|
48 |
+
"num_timesteps": 5000192,
|
49 |
+
"_total_timesteps": 5000000,
|
50 |
+
"_num_timesteps_at_start": 0,
|
51 |
+
"seed": null,
|
52 |
+
"action_noise": null,
|
53 |
+
"start_time": 1656909885.5959098,
|
54 |
+
"learning_rate": 0.0003,
|
55 |
+
"tensorboard_log": null,
|
56 |
+
"lr_schedule": {
|
57 |
+
":type:": "<class 'function'>",
|
58 |
+
":serialized:": "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"
|
59 |
+
},
|
60 |
+
"_last_obs": null,
|
61 |
+
"_last_episode_starts": {
|
62 |
+
":type:": "<class 'numpy.ndarray'>",
|
63 |
+
":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
|
64 |
+
},
|
65 |
+
"_last_original_obs": null,
|
66 |
+
"_episode_num": 0,
|
67 |
+
"use_sde": false,
|
68 |
+
"sde_sample_freq": -1,
|
69 |
+
"_current_progress_remaining": -3.8399999999993994e-05,
|
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": 19532,
|
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 |
+
}
|
ppo-MountainCar-MlpPolicy-v2/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:1fb9087b6adc8872f7ed0e740c46b0361356c8279dc991d49b2dc2ecc8a53964
|
3 |
+
size 80889
|
ppo-MountainCar-MlpPolicy-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:5762288fe4b6f2f1f55ab82aee0eec7301aa3929ca42ee030c7db9571fb37018
|
3 |
+
size 39745
|
ppo-MountainCar-MlpPolicy-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-MountainCar-MlpPolicy-v2/system_info.txt
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
OS: macOS-10.15.7-x86_64-i386-64bit Darwin Kernel Version 19.6.0: Mon Aug 31 22:12:52 PDT 2020; root:xnu-6153.141.2~1/RELEASE_X86_64
|
2 |
+
Python: 3.8.5
|
3 |
+
Stable-Baselines3: 1.5.0
|
4 |
+
PyTorch: 1.12.0
|
5 |
+
GPU Enabled: False
|
6 |
+
Numpy: 1.23.0
|
7 |
+
Gym: 0.24.1
|
replay.mp4
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:4a04bb40be3202ee9cd856532819f2bc07d8a47da19566d1f0761c091be3328a
|
3 |
+
size 223448
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": -116.1, "std_reward": 13.678084661238211, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-07-04T12:15:36.553481"}
|