Upload PPO LunarLander-v2 trained agent
Browse files- .gitattributes +1 -0
- 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 +3 -0
- results.json +1 -0
.gitattributes
CHANGED
|
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
| 33 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
| 33 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
| 36 |
+
replay.mp4 filter=lfs diff=lfs merge=lfs -text
|
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: 248.85 +/- 38.51
|
| 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 0x7fe91806fb00>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe91806fba0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe91806fc40>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe91806fce0>", "_build": "<function ActorCriticPolicy._build at 0x7fe91806fd80>", "forward": "<function ActorCriticPolicy.forward at 0x7fe91806fe20>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7fe91806fec0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe91806ff60>", "_predict": "<function ActorCriticPolicy._predict at 0x7fe918070040>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe9180700e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe918070180>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe918070220>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fe918127980>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1765268939873182331, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVhAAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksQhZSMAUOUdJRSlC4="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "_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": 310, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "gAWVdwIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBNudW1weS5fY29yZS5udW1lcmljlIwLX2Zyb21idWZmZXKUk5QolggAAAAAAAAAAQEBAQEBAQGUaAiMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUjAFDlHSUUpSMDWJvdW5kZWRfYWJvdmWUaBEolggAAAAAAAAAAQEBAQEBAQGUaBVLCIWUaBl0lFKUjAZfc2hhcGWUSwiFlIwDbG93lGgRKJYgAAAAAAAAAAAAtMIAALTCAACgwAAAoMDbD0nAAACgwAAAAIAAAACAlGgLSwiFlGgZdJRSlIwEaGlnaJRoESiWIAAAAAAAAAAAALRCAAC0QgAAoEAAAKBA2w9JQAAAoEAAAIA/AACAP5RoC0sIhZRoGXSUUpSMCGxvd19yZXBylIxbWy05MC4gICAgICAgIC05MC4gICAgICAgICAtNS4gICAgICAgICAtNS4gICAgICAgICAtMy4xNDE1OTI3ICAtNS4KICAtMC4gICAgICAgICAtMC4gICAgICAgXZSMCWhpZ2hfcmVwcpSMU1s5MC4gICAgICAgIDkwLiAgICAgICAgIDUuICAgICAgICAgNS4gICAgICAgICAzLjE0MTU5MjcgIDUuCiAgMS4gICAgICAgICAxLiAgICAgICBdlIwKX25wX3JhbmRvbZROdWIu", "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:": "gAWV3AAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFm51bXB5Ll9jb3JlLm11bHRpYXJyYXmUjAZzY2FsYXKUk5SMBW51bXB5lIwFZHR5cGWUk5SMAmk4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJDCAQAAAAAAAAAlIaUUpSMBXN0YXJ0lGgIaA5DCAAAAAAAAAAAlIaUUpSMBl9zaGFwZZQpjAVkdHlwZZRoDowKX25wX3JhbmRvbZROdWIu", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "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, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "system_info": {"OS": "Linux-6.6.105+-x86_64-with-glibc2.35 # 1 SMP Thu Oct 2 10:42:05 UTC 2025", "Python": "3.12.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.9.0+cu126", "GPU Enabled": "True", "Numpy": "2.0.2", "Cloudpickle": "3.1.2", "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 sha256:822520a35bde685283b4598fbf87c841ad2d89b8861fc6a494ebc9f97a1223f5
|
| 3 |
+
size 149142
|
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 0x7fe91806fb00>",
|
| 8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7fe91806fba0>",
|
| 9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7fe91806fc40>",
|
| 10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7fe91806fce0>",
|
| 11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7fe91806fd80>",
|
| 12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7fe91806fe20>",
|
| 13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7fe91806fec0>",
|
| 14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7fe91806ff60>",
|
| 15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7fe918070040>",
|
| 16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7fe9180700e0>",
|
| 17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7fe918070180>",
|
| 18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7fe918070220>",
|
| 19 |
+
"__abstractmethods__": "frozenset()",
|
| 20 |
+
"_abc_impl": "<_abc._abc_data object at 0x7fe918127980>"
|
| 21 |
+
},
|
| 22 |
+
"verbose": 1,
|
| 23 |
+
"policy_kwargs": {},
|
| 24 |
+
"num_timesteps": 1015808,
|
| 25 |
+
"_total_timesteps": 1000000,
|
| 26 |
+
"_num_timesteps_at_start": 0,
|
| 27 |
+
"seed": null,
|
| 28 |
+
"action_noise": null,
|
| 29 |
+
"start_time": 1765268939873182331,
|
| 30 |
+
"learning_rate": 0.0003,
|
| 31 |
+
"tensorboard_log": null,
|
| 32 |
+
"_last_obs": {
|
| 33 |
+
":type:": "<class 'numpy.ndarray'>",
|
| 34 |
+
":serialized:": "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"
|
| 35 |
+
},
|
| 36 |
+
"_last_episode_starts": {
|
| 37 |
+
":type:": "<class 'numpy.ndarray'>",
|
| 38 |
+
":serialized:": "gAWVhAAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksQhZSMAUOUdJRSlC4="
|
| 39 |
+
},
|
| 40 |
+
"_last_original_obs": null,
|
| 41 |
+
"_episode_num": 0,
|
| 42 |
+
"use_sde": false,
|
| 43 |
+
"sde_sample_freq": -1,
|
| 44 |
+
"_current_progress_remaining": -0.015808000000000044,
|
| 45 |
+
"_stats_window_size": 100,
|
| 46 |
+
"ep_info_buffer": {
|
| 47 |
+
":type:": "<class 'collections.deque'>",
|
| 48 |
+
":serialized:": "gAWVJgwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQHDqvIXCTECMAWyUS/mMAXSUR0CZYlnP3SKFdX2UKGgGR0BxspRfnfVJaAdNIgFoCEdAmWKxlHz6J3V9lChoBkdAZHyZF5OafGgHTegDaAhHQJlj6VopQUJ1fZQoaAZHQGLLv9UCJXRoB03oA2gIR0CZY/cZ9/jLdX2UKGgGR0BwJ8/KQq7RaAdNFwFoCEdAmWQVy3kPtnV9lChoBkdASRG+ueSSvGgHS9BoCEdAmWQel41P33V9lChoBkdAZHZqkdmxuGgHTegDaAhHQJlkOH9FWn11fZQoaAZHQHGCeSOinHhoB00/AWgIR0CZZrwLVnVYdX2UKGgGR0Bvcc9U0elsaAdL/2gIR0CZZscKgIyCdX2UKGgGR0Bwmd4u9OARaAdL/GgIR0CZZuPczqKQdX2UKGgGR0Bul5LuhK15aAdNAgFoCEdAmWtRDw6QvHV9lChoBkdAbJPr433pOmgHS/ZoCEdAmWuSwbEP2HV9lChoBkdAcyj6tknTiWgHTUsBaAhHQJnEgUuctoV1fZQoaAZHQG66U5MlC1JoB0v4aAhHQJnFaLl3hXN1fZQoaAZHQG5uy5I6KcdoB00cAWgIR0CZxatLL6k7dX2UKGgGR0Bt2U/+sHSnaAdL+mgIR0CZxcryDqW1dX2UKGgGR0BtFrzqbBoFaAdL/WgIR0CZxcurIYFadX2UKGgGR0BcW7di2DxtaAdN6ANoCEdAmcYDRMN+b3V9lChoBkdAcV7gogFHKGgHTRMBaAhHQJnGh2/zreJ1fZQoaAZHQHB7CAtnPE9oB00tAWgIR0CZx0S5RTCMdX2UKGgGR0BxybssxwhoaAdNCQFoCEdAmcj1a8pTdnV9lChoBkdAbhU4o7V8TmgHTRYBaAhHQJnJVhJAdGR1fZQoaAZHQG9E39JjDsNoB00fAWgIR0CZyaUXpGF0dX2UKGgGR0BjZKTpxFRYaAdN6ANoCEdAmcquskpqh3V9lChoBkdAcCWqTKT0QWgHS/FoCEdAmcxpgLJCB3V9lChoBkdAchYSfUWl/GgHS95oCEdAmc0xaX8fm3V9lChoBkdAcNVIF/x2CGgHTRsBaAhHQJnNx4TsY2t1fZQoaAZHQHABO0svqTtoB0vuaAhHQJnOI3R5TqB1fZQoaAZHQG7xavaDf3xoB0v1aAhHQJnOYT+NtIl1fZQoaAZHQHDPCBoVVPxoB01LA2gIR0CZz1HLA57xdX2UKGgGR0BxVD/bTMJQaAdNCwFoCEdAmc9bXUYsNHV9lChoBkdAbw59AHE/B2gHS/hoCEdAmc/1j7Q9inV9lChoBkdAcHnUrCm/FmgHTQ4BaAhHQJnP9efI0ZZ1fZQoaAZHQHAtlFH8TBZoB01PAWgIR0CZ0HAxzq8ldX2UKGgGR0BraqOWBz3iaAdL/GgIR0CZ0dpQUHpsdX2UKGgGR0ByLdOUMXrMaAdL+2gIR0CZ0g/p+tr9dX2UKGgGR0BwYrk1dgOSaAdNAAFoCEdAmdUjOPeYUnV9lChoBkdAcFuiRGMGYGgHTQcBaAhHQJnWX5VOsT51fZQoaAZHQHKFm4iHIp9oB00AAWgIR0CZ13yZrpJPdX2UKGgGR0BwfOr92ovSaAdNHwFoCEdAmdgijYZl4HV9lChoBkdAbryPCEYfn2gHS/poCEdAmdh4tQKrrHV9lChoBkdARBkwFkhA4WgHS99oCEdAmdi/GMn7YXV9lChoBkdAcGGrGR3eN2gHTSkBaAhHQJnY8Qz1sch1fZQoaAZHQHA2M2NvOyFoB0v/aAhHQJnZXt4RmK91fZQoaAZHQG5M+GXXyy5oB00dAWgIR0CZ3Yl41P30dX2UKGgGR0Bip+/pMYdiaAdN6ANoCEdAmd8Mk2P1c3V9lChoBkdAcCgo8p1A7mgHTQwBaAhHQJnh0KG+K0l1fZQoaAZHQHDAq64Ds+poB0v5aAhHQJniYNlRP451fZQoaAZHQG+GKRU3n6loB0v7aAhHQJnmSTs6aLJ1fZQoaAZHQG30D4593KVoB0v1aAhHQJnmhn27FsJ1fZQoaAZHQG0MLgn+hoNoB00rAWgIR0CZ5vrnTy8SdX2UKGgGR0Bu/qZa3ZwoaAdNFQFoCEdAmedL56+nInV9lChoBkdAb3Tf9gnc+WgHTTwBaAhHQJnoRf5ULlV1fZQoaAZHQHCZ+6/Zdv9oB003AWgIR0CZ6GRBeHBUdX2UKGgGR0BunsmtyPuHaAdL+WgIR0CZ6r4VymygdX2UKGgGR0BwTvowEhaDaAdNCgFoCEdAmexo3rD633V9lChoBkdAcb6kWykbgmgHTQABaAhHQJnt21YyO7x1fZQoaAZHQHEkABgeA/doB00EAWgIR0CZ7mCwbEP2dX2UKGgGR0BgNP4ZdfLLaAdN6ANoCEdAme7hLTQVsXV9lChoBkdAcu4nXNC7b2gHTcoCaAhHQJnvPJPqLTB1fZQoaAZHQHC+MjVx0dRoB0v7aAhHQJnwnbEgntx1fZQoaAZHQG5NU2LpA2RoB0v6aAhHQJnxcehf0Ep1fZQoaAZHQG9P+j2zv7ZoB00MAWgIR0CZ8eCsfaHsdX2UKGgGR0BuS38CPp6haAdNKAFoCEdAmfKbzTWoWHV9lChoBkdAYz68scyWRmgHTegDaAhHQJnyzM4cWCV1fZQoaAZHQGbkOavzOHFoB03oA2gIR0CZ9LCEYfnwdX2UKGgGR0BxBOZtvXK9aAdNFAFoCEdAmfeHxBmf5HV9lChoBkdAcBbxoqTbFmgHTUgBaAhHQJn3+2w3YL91fZQoaAZHQG4G7u+h4+toB0v5aAhHQJn46JLuhK11fZQoaAZHQFxt3jMmnfloB03oA2gIR0CZ+flVcUuddX2UKGgGR0BwzHwc5sCUaAdNIwFoCEdAmfohMnJDE3V9lChoBkdAbn9YraufVmgHS/loCEdAmfqCCaqjrXV9lChoBkdAcg5v6TGHYmgHTUgBaAhHQJn8U/UvwmV1fZQoaAZHQG7lJeu3c59oB00LAWgIR0CZ/GhBZ6lddX2UKGgGR0BgEIjKPn0TaAdN6ANoCEdAmf0ewC8vmHV9lChoBkdAcDYlXzUZvWgHTRoBaAhHQJn95QZXMhZ1fZQoaAZHQHCjojv/io9oB002AWgIR0CZ/r+3H7xedX2UKGgGR0BwhNdnkDISaAdNDgFoCEdAmf9KziS7oXV9lChoBkdAcMmjL0SRKmgHS/FoCEdAmgJI/FBIF3V9lChoBkdAa2IO4G2TgWgHTQIBaAhHQJoEY03wTdt1fZQoaAZHQHG5Ez0pVjtoB00gAWgIR0CaBZXfqHGkdX2UKGgGR0BwzcJdB0IUaAdL7WgIR0CaBxEQoTf0dX2UKGgGR0BuqvHcUM5PaAdNAgFoCEdAmgcTq0MPSXV9lChoBkdAckroQ4CIUWgHTQsBaAhHQJoHkLLIPsl1fZQoaAZHQHBoLhzeXRhoB01PAWgIR0CaCGoVmBe5dX2UKGgGR0BwZi7UXpGGaAdNHwFoCEdAmgs8ySFGonV9lChoBkdAb3F7rs0HhWgHTRIBaAhHQJoLVT1kDp11fZQoaAZHQHIxSW/rSmZoB03ZAWgIR0CaC8jqOcUedX2UKGgGR0Bsud12aDwpaAdL9mgIR0CaDVGNJe3QdX2UKGgGR0BxknQv6CUYaAdL9mgIR0CaEawGnn+ydX2UKGgGR0BX4Pi5uqFRaAdN6ANoCEdAmhNFs+FDfHV9lChoBkdAYjHd8iOea2gHTegDaAhHQJoTfIeYD1Z1fZQoaAZHQHFheJP69ChoB00tAWgIR0CaE4zq8lHCdX2UKGgGR0Bt9odKdxyXaAdNDQFoCEdAmharROUMX3V9lChoBkdAcGNKjzqbB2gHTSEBaAhHQJoWtnoPkJd1fZQoaAZHQHAv0SVW0Z5oB01VAWgIR0CaGGSV4X41dX2UKGgGR0Buq/tjTa0yaAdNHAFoCEdAmhoQIppeu3V9lChoBkdAY3QyGi5/b2gHTegDaAhHQJoaJ2Pkq+d1fZQoaAZHQHH2XevZAY5oB00jAWgIR0CaGkbQTmGNdX2UKGgGR0Buif3BYV7AaAdNBwFoCEdAmhs0Gmk30nVlLg=="
|
| 49 |
+
},
|
| 50 |
+
"ep_success_buffer": {
|
| 51 |
+
":type:": "<class 'collections.deque'>",
|
| 52 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
| 53 |
+
},
|
| 54 |
+
"_n_updates": 310,
|
| 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:": "gAWV3AAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFm51bXB5Ll9jb3JlLm11bHRpYXJyYXmUjAZzY2FsYXKUk5SMBW51bXB5lIwFZHR5cGWUk5SMAmk4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJDCAQAAAAAAAAAlIaUUpSMBXN0YXJ0lGgIaA5DCAAAAAAAAAAAlIaUUpSMBl9zaGFwZZQpjAVkdHlwZZRoDowKX25wX3JhbmRvbZROdWIu",
|
| 73 |
+
"n": "4",
|
| 74 |
+
"start": "0",
|
| 75 |
+
"_shape": [],
|
| 76 |
+
"dtype": "int64",
|
| 77 |
+
"_np_random": null
|
| 78 |
+
},
|
| 79 |
+
"n_envs": 16,
|
| 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 |
+
"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:fbb38ca90ea0bdd562b2b8dbb733b2f5f2a0bb3622356ba48b9c489bdcdd466b
|
| 3 |
+
size 88695
|
ppo-LunarLander-v2/policy.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:b430522a43ac0cb86c6039acfe793f9c6afa909889f110e13fd43064b14f78a6
|
| 3 |
+
size 44095
|
ppo-LunarLander-v2/pytorch_variables.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:07c7431cf6005e7d8f367d79e995f63e2f9b981a37e3437b795d058f9af4308b
|
| 3 |
+
size 1261
|
ppo-LunarLander-v2/system_info.txt
ADDED
|
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
- OS: Linux-6.6.105+-x86_64-with-glibc2.35 # 1 SMP Thu Oct 2 10:42:05 UTC 2025
|
| 2 |
+
- Python: 3.12.12
|
| 3 |
+
- Stable-Baselines3: 2.0.0a5
|
| 4 |
+
- PyTorch: 2.9.0+cu126
|
| 5 |
+
- GPU Enabled: True
|
| 6 |
+
- Numpy: 2.0.2
|
| 7 |
+
- Cloudpickle: 3.1.2
|
| 8 |
+
- Gymnasium: 0.28.1
|
| 9 |
+
- OpenAI Gym: 0.25.2
|
replay.mp4
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d29c7fbc54f77eac6a2211fc53ded243bbc889b5a51f5c9649061ffe96be26a7
|
| 3 |
+
size 170193
|
results.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"mean_reward": 248.8479494, "std_reward": 38.51401075405209, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-12-09T09:14:47.156368"}
|