Upload PPO LunarLander-v2 trained agent
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 +99 -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 +9 -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: 214.52 +/- 71.33
|
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 0x7f9ec6cb2cb0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9ec6cb2d40>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9ec6cb2dd0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9ec6cb2e60>", "_build": "<function ActorCriticPolicy._build at 0x7f9ec6cb2ef0>", "forward": "<function ActorCriticPolicy.forward at 0x7f9ec6cb2f80>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f9ec6cb3010>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9ec6cb30a0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f9ec6cb3130>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9ec6cb31c0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f9ec6cb3250>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9ec6cb32e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7f9ec6c53000>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1000448, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1707464832616981436, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAJplM70pOAy6c71VuekAQrSpSHU6XcZ/OAAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="}, "_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": -0.00044800000000000395, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 3960, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True True True True True True True True]", "bounded_above": "[ True True True True True True True True]", "_shape": [8], "low": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]", "high": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]", "low_repr": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]", "high_repr": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 1, "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, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.1.0+cu121", "GPU Enabled": "False", "Numpy": "1.23.5", "Cloudpickle": "2.2.1", "Gymnasium": "0.28.1", "OpenAI Gym": "0.25.2"}}
|
ppo-LunarLander-v2.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:55105b8b21681fc0407de94c5dbcc8ec951d14d859d266e88fe332fce94f7428
|
3 |
+
size 146899
|
ppo-LunarLander-v2/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
2.0.0a5
|
ppo-LunarLander-v2/data
ADDED
@@ -0,0 +1,99 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 0x7f9ec6cb2cb0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f9ec6cb2d40>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f9ec6cb2dd0>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f9ec6cb2e60>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f9ec6cb2ef0>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f9ec6cb2f80>",
|
13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7f9ec6cb3010>",
|
14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f9ec6cb30a0>",
|
15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f9ec6cb3130>",
|
16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f9ec6cb31c0>",
|
17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f9ec6cb3250>",
|
18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f9ec6cb32e0>",
|
19 |
+
"__abstractmethods__": "frozenset()",
|
20 |
+
"_abc_impl": "<_abc._abc_data object at 0x7f9ec6c53000>"
|
21 |
+
},
|
22 |
+
"verbose": 1,
|
23 |
+
"policy_kwargs": {},
|
24 |
+
"num_timesteps": 1000448,
|
25 |
+
"_total_timesteps": 1000000,
|
26 |
+
"_num_timesteps_at_start": 0,
|
27 |
+
"seed": null,
|
28 |
+
"action_noise": null,
|
29 |
+
"start_time": 1707464832616981436,
|
30 |
+
"learning_rate": 0.0003,
|
31 |
+
"tensorboard_log": null,
|
32 |
+
"_last_obs": {
|
33 |
+
":type:": "<class 'numpy.ndarray'>",
|
34 |
+
":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAJplM70pOAy6c71VuekAQrSpSHU6XcZ/OAAAgD8AAIA/lIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksBSwiGlIwBQ5R0lFKULg=="
|
35 |
+
},
|
36 |
+
"_last_episode_starts": {
|
37 |
+
":type:": "<class 'numpy.ndarray'>",
|
38 |
+
":serialized:": "gAWVdAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYBAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSwGFlIwBQ5R0lFKULg=="
|
39 |
+
},
|
40 |
+
"_last_original_obs": null,
|
41 |
+
"_episode_num": 0,
|
42 |
+
"use_sde": false,
|
43 |
+
"sde_sample_freq": -1,
|
44 |
+
"_current_progress_remaining": -0.00044800000000000395,
|
45 |
+
"_stats_window_size": 100,
|
46 |
+
"ep_info_buffer": {
|
47 |
+
":type:": "<class 'collections.deque'>",
|
48 |
+
":serialized:": "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"
|
49 |
+
},
|
50 |
+
"ep_success_buffer": {
|
51 |
+
":type:": "<class 'collections.deque'>",
|
52 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
53 |
+
},
|
54 |
+
"_n_updates": 3960,
|
55 |
+
"observation_space": {
|
56 |
+
":type:": "<class 'gymnasium.spaces.box.Box'>",
|
57 |
+
":serialized:": "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",
|
58 |
+
"dtype": "float32",
|
59 |
+
"bounded_below": "[ True True True True True True True True]",
|
60 |
+
"bounded_above": "[ True True True True True True True True]",
|
61 |
+
"_shape": [
|
62 |
+
8
|
63 |
+
],
|
64 |
+
"low": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]",
|
65 |
+
"high": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]",
|
66 |
+
"low_repr": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]",
|
67 |
+
"high_repr": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]",
|
68 |
+
"_np_random": null
|
69 |
+
},
|
70 |
+
"action_space": {
|
71 |
+
":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
|
72 |
+
":serialized:": "gAWV1QAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCloCmgOjApfbnBfcmFuZG9tlE51Yi4=",
|
73 |
+
"n": "4",
|
74 |
+
"start": "0",
|
75 |
+
"_shape": [],
|
76 |
+
"dtype": "int64",
|
77 |
+
"_np_random": null
|
78 |
+
},
|
79 |
+
"n_envs": 1,
|
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 |
+
"lr_schedule": {
|
96 |
+
":type:": "<class 'function'>",
|
97 |
+
":serialized:": "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"
|
98 |
+
}
|
99 |
+
}
|
ppo-LunarLander-v2/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:3c7cd3472d6ba203a879fa1b493b95bafca7a4d3b67725ab384863253b7cf4bb
|
3 |
+
size 87978
|
ppo-LunarLander-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:1e46e6b0f8d1ea7617b9da5a91bbe27bb8ddf39e7651cff05cd5a20133fca97b
|
3 |
+
size 43634
|
ppo-LunarLander-v2/pytorch_variables.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:0c35cea3b2e60fb5e7e162d3592df775cd400e575a31c72f359fb9e654ab00c5
|
3 |
+
size 864
|
ppo-LunarLander-v2/system_info.txt
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
- OS: Linux-6.1.58+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sat Nov 18 15:31:17 UTC 2023
|
2 |
+
- Python: 3.10.12
|
3 |
+
- Stable-Baselines3: 2.0.0a5
|
4 |
+
- PyTorch: 2.1.0+cu121
|
5 |
+
- GPU Enabled: False
|
6 |
+
- Numpy: 1.23.5
|
7 |
+
- Cloudpickle: 2.2.1
|
8 |
+
- Gymnasium: 0.28.1
|
9 |
+
- OpenAI Gym: 0.25.2
|
replay.mp4
ADDED
Binary file (200 kB). View file
|
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 214.5245825, "std_reward": 71.33319445834584, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-02-09T08:13:35.926836"}
|