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: 211.06 +/- 93.45
|
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 0x7ae1b96f8f70>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ae1b96f9000>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ae1b96f9090>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ae1b96f9120>", "_build": "<function ActorCriticPolicy._build at 0x7ae1b96f91b0>", "forward": "<function ActorCriticPolicy.forward at 0x7ae1b96f9240>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ae1b96f92d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ae1b96f9360>", "_predict": "<function ActorCriticPolicy._predict at 0x7ae1b96f93f0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ae1b96f9480>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ae1b96f9510>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ae1b96f95a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ae1b969fe00>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1000448, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1709682937193490776, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAI1M1z17+o26Km7cuhXnIrbD0uc5s5/+OQAAAAAAAIA/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": 3908, "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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "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": "True", "Numpy": "1.25.2", "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 sha256:8211325d7e09c02bb2d4eb93f532b035d7559849f48d80521051d34a2682bbb1
|
3 |
+
size 147414
|
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 0x7ae1b96f8f70>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ae1b96f9000>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ae1b96f9090>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ae1b96f9120>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7ae1b96f91b0>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7ae1b96f9240>",
|
13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7ae1b96f92d0>",
|
14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ae1b96f9360>",
|
15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7ae1b96f93f0>",
|
16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ae1b96f9480>",
|
17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ae1b96f9510>",
|
18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7ae1b96f95a0>",
|
19 |
+
"__abstractmethods__": "frozenset()",
|
20 |
+
"_abc_impl": "<_abc._abc_data object at 0x7ae1b969fe00>"
|
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": 1709682937193490776,
|
30 |
+
"learning_rate": 0.0003,
|
31 |
+
"tensorboard_log": null,
|
32 |
+
"_last_obs": {
|
33 |
+
":type:": "<class 'numpy.ndarray'>",
|
34 |
+
":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAI1M1z17+o26Km7cuhXnIrbD0uc5s5/+OQAAAAAAAIA/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:": "gAWVOQwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQHEf2LtNSIiMAWyUTXIBjAF0lEdAmwHp3cHnlnV9lChoBkdAcSJTjebd8GgHTTABaAhHQJsDnU/fO2R1fZQoaAZHQG5jjnNgSe1oB01MAWgIR0CbBoiGFi8WdX2UKGgGR0Bu+02BJ7LMaAdNiwFoCEdAmwi+Aqd6LXV9lChoBkdAcH6TpxFRYWgHTWQBaAhHQJsKtRtP5591fZQoaAZHQHD8+E7GNrFoB02MAmgIR0CbD8P1tfoidX2UKGgGR0BxTU7tAs06aAdNQgFoCEdAmxIG6PKdQXV9lChoBkdAabLiMo+fRWgHTUwBaAhHQJsWHNiYsup1fZQoaAZHQHGKnVsk6cRoB01HAWgIR0CbGGk5IYm+dX2UKGgGR0BxIe4OMERraAdNkAFoCEdAmxqTY7JXAHV9lChoBkdAbnpyBkI5YGgHTUoBaAhHQJsdjSuyNXJ1fZQoaAZHQHInHfyf+S9oB009AWgIR0CbH1QL/jsEdX2UKGgGR0BvXgpBomG/aAdNbAFoCEdAmyFzK9wm3XV9lChoBkdAb9szRhMJyGgHTUABaAhHQJskZ6nivPl1fZQoaAZHQFOslXiiqQ1oB03oA2gIR0CbK0WGATZhdX2UKGgGR0Bw1JsVLzwuaAdNJQFoCEdAmyzuAEt/WnV9lChoBkdAcDPFVDKHPGgHTW8BaAhHQJsu8I7eVLV1fZQoaAZHQHI+BR/EwWZoB02bAWgIR0CbMmRgZ0jkdX2UKGgGR0BKi1bJOnEVaAdL3GgIR0CbM51CPZIydX2UKGgGR0BwxnHvMKTjaAdNJQFoCEdAmzVNA9mpVHV9lChoBkdAcdfwiaAnUmgHTUIBaAhHQJs3IgbIcR11fZQoaAZHQHFFafOD8LtoB01JAWgIR0CbOiHt4RmLdX2UKGgGR0BtODGipNsWaAdNVQFoCEdAmzwFFhG6PXV9lChoBkdAcg+1IiC8OGgHTQUBaAhHQJs9gE0SAYp1fZQoaAZHQHJ+r+HaewtoB01jAWgIR0CbQP/Pw/gSdX2UKGgGR0BxF1bX6InCaAdNKwFoCEdAm0MrKifxt3V9lChoBkdAbUvJvo/zKGgHTV8BaAhHQJtFxsWO6up1fZQoaAZHQG9PlpXZGrloB00YAWgIR0CbSS9A5aNddX2UKGgGR0BzF23Zwn6VaAdNGgFoCEdAm0q1RpDeCXV9lChoBkdAbr51+RYA82gHTTkBaAhHQJtMdm/WUbF1fZQoaAZHQG8KDmjj7yhoB01BAWgIR0CbTkGSZBszdX2UKGgGR0Bv5Nx0dRzjaAdNRgFoCEdAm1FSOvMbFXV9lChoBkdAcg+pAD7qIWgHTSYCaAhHQJtUSOBDohZ1fZQoaAZHQHMYwZsKsuFoB00jAWgIR0CbVyhB7eEadX2UKGgGR0Bw4LZVXFLnaAdNPgFoCEdAm1jmknCwbHV9lChoBkdAcKBdRR/EwWgHTTUBaAhHQJtaoOI68xt1fZQoaAZHQG/JlsYVIqdoB02KAWgIR0CbXgicoYvWdX2UKGgGR0BtJcZxaPjoaAdNJgFoCEdAm1+veUILPXV9lChoBkdAQtY9q1w5vWgHS8ZoCEdAm2DMqBmPHXV9lChoBkdAcD5Hh0hePmgHTY0BaAhHQJtjCFAVwgl1fZQoaAZHQHE/KekHlfZoB00mAWgIR0CbZeQZ4wAVdX2UKGgGR0BwV3rqt5lfaAdNNgFoCEdAm2eQjUutfXV9lChoBkdAcD7uOS4e92gHTWABaAhHQJtpjmmtQsR1fZQoaAZHQG9qJpN9H+ZoB01+AWgIR0CbbNqwhW5pdX2UKGgGR0Ay/WSEDhcaaAdL/WgIR0Cbbjo99tuUdX2UKGgGR0BwvO2F36hyaAdNOgFoCEdAm2/2qLjxTnV9lChoBkdARKw+8oQWe2gHS/FoCEdAm3Mpj2BatHV9lChoBkdAcVb7ZWaMJmgHTakCaAhHQJt4d4yGi6B1fZQoaAZHQHBHSauwHJNoB00sAWgIR0Cbeh3BYV7AdX2UKGgGR0BLIN3np0OmaAdNDAFoCEdAm3y+uq3mWHV9lChoBkdAcLq7/GVAzGgHTVQBaAhHQJt+pVghKUV1fZQoaAZHQHB32tITXatoB005AWgIR0CbgFzBAOawdX2UKGgGR0BxCpWFN+LFaAdNaQFoCEdAm4ODWoWHlHV9lChoBkdAbqFjU/fO2WgHTSABaAhHQJuFIw9JSR91fZQoaAZHQHGZDPa+N99oB01EAWgIR0Cbhx5myxA0dX2UKGgGR0BwX5OvdM0xaAdNLQFoCEdAm4oBbOeJ53V9lChoBkdAcfW/TLGJemgHTXQBaAhHQJuMEPlMh5h1fZQoaAZHQHDG1SCOFQFoB00ZAWgIR0CbjZqBVdX1dX2UKGgGR0BwPyrS3LFGaAdNPgFoCEdAm5B89SuQqHV9lChoBkdAcWabqyGBWmgHTUwBaAhHQJuSSvV3EAJ1fZQoaAZHQHDbdbcGkepoB00yAWgIR0CblAFNL128dX2UKGgGR0BykzbItDlYaAdNTAFoCEdAm5b+IMz/InV9lChoBkdASMMrmQr+YWgHS99oCEdAm5hGoWHk93V9lChoBkdAc7foNutOmGgHTYABaAhHQJuaZu0kWyl1fZQoaAZHQHIKMguAZsNoB00tAWgIR0CbnB3Dej20dX2UKGgGR0BwhmpuMuOCaAdNZAFoCEdAm59cAq/dqXV9lChoBkdAbWqrbQC0W2gHTTUBaAhHQJuhdBPbfxd1fZQoaAZHQEhgWldkauRoB0v0aAhHQJujKR+z+m51fZQoaAZHQHBQyro4dZJoB001AWgIR0CbpvRQaaTfdX2UKGgGR0Bw3dc9nscAaAdNVgFoCEdAm6mHO8kD6nV9lChoBkdAcRqLl3hXKmgHTVABaAhHQJurZLmITGp1fZQoaAZHQHBdhoysS01oB01XAWgIR0Cbrnby6MBIdX2UKGgGR0Byo2c0+C9RaAdNRQFoCEdAm7BMUIsyz3V9lChoBkdAbVidGy5ZsGgHTVABaAhHQJuyLvb48EF1fZQoaAZHQE800gr6LwZoB0vfaAhHQJuzaOyVv/B1fZQoaAZHQHBxMkY4yXVoB01LAWgIR0Cbtm9F4LThdX2UKGgGR0Bw2osOG0u2aAdNHQFoCEdAm7gJxaPjn3V9lChoBkdAcW1vM8ox6GgHTa4BaAhHQJu6cVsUIs11fZQoaAZHQHGjcfRu0kZoB00PAWgIR0CbvTSLIgeSdX2UKGgGR0BHN+z+m3vyaAdL9GgIR0CbvodPtUn5dX2UKGgGR0BwkQczZYgaaAdNWwFoCEdAm8B5XQtz0nV9lChoBkdAbvjMRHww02gHTToBaAhHQJvDTdxhlUZ1fZQoaAZHQEmOxTsIE8toB0voaAhHQJvElbjcVQB1fZQoaAZHQHBw1p48loloB014AWgIR0CbxqNMoMKDdX2UKGgGR0BLrwC8vmHQaAdL9GgIR0Cbx/zTF2mpdX2UKGgGR0BwSAUDdP+GaAdNXQFoCEdAm8sP8EV32XV9lChoBkdAb5kbmU4aP2gHTTkBaAhHQJvMzoV2zOZ1fZQoaAZHQEBgmE4//vRoB0vIaAhHQJvN6zt1IRR1fZQoaAZHQHCBWdVea8ZoB01BAWgIR0Cb0OYUnG83dX2UKGgGR0Bt1ei5/b0waAdNLQFoCEdAm9MIPGyX2XV9lChoBkdAcCcA+6iCa2gHTTkBaAhHQJvVP0HyEtd1fZQoaAZHQHANEHQhOgxoB004AWgIR0Cb2WxzJZGKdX2UKGgGR0BuEfO2RaHLaAdNOQFoCEdAm9skHQhOg3V9lChoBkdAQkySTyJ9A2gHTRIBaAhHQJvcp8BuGbl1fZQoaAZHQHEVqhHskY5oB000AWgIR0Cb3mfRu0kXdX2UKGgGR0By2O/CZWq+aAdNIQFoCEdAm+E5Z8rqdHV9lChoBkdAcnVI1cdHUmgHTTQBaAhHQJvi504iosJ1fZQoaAZHQHAN2za9K29oB005AWgIR0Cb5K5f+jubdX2UKGgGR0BmVTRD1GsnaAdN6ANoCEdAm+uAA+6iCnV9lChoBkdAcH0yZ8a4t2gHTVUBaAhHQJvumcUdq+J1ZS4="
|
49 |
+
},
|
50 |
+
"ep_success_buffer": {
|
51 |
+
":type:": "<class 'collections.deque'>",
|
52 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
53 |
+
},
|
54 |
+
"_n_updates": 3908,
|
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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=",
|
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 sha256:22997789f28e3402fe08491d06ab5c9404f1c02edb429e213c17c97514059f8a
|
3 |
+
size 88362
|
ppo-LunarLander-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ce05aa889ae2a95c05d1ea72aa7749e105839e52d669f0ab1ac3d52e8a858b8f
|
3 |
+
size 43762
|
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: True
|
6 |
+
- Numpy: 1.25.2
|
7 |
+
- Cloudpickle: 2.2.1
|
8 |
+
- Gymnasium: 0.28.1
|
9 |
+
- OpenAI Gym: 0.25.2
|
replay.mp4
ADDED
Binary file (154 kB). View file
|
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 211.0578536142019, "std_reward": 93.45009320105886, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-03-06T00:50:24.015429"}
|