Upload PPO LunarLander-v3 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-v3
|
| 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-v3
|
| 16 |
+
type: LunarLander-v3
|
| 17 |
+
metrics:
|
| 18 |
+
- type: mean_reward
|
| 19 |
+
value: 235.75 +/- 63.14
|
| 20 |
+
name: mean_reward
|
| 21 |
+
verified: false
|
| 22 |
+
---
|
| 23 |
+
|
| 24 |
+
# **PPO** Agent playing **LunarLander-v3**
|
| 25 |
+
This is a trained model of a **PPO** agent playing **LunarLander-v3**
|
| 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 0x7dca315491c0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7dca31549260>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7dca31549300>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7dca315493a0>", "_build": "<function ActorCriticPolicy._build at 0x7dca31549440>", "forward": "<function ActorCriticPolicy.forward at 0x7dca315494e0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7dca31549580>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7dca31549620>", "_predict": "<function ActorCriticPolicy._predict at 0x7dca315496c0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7dca31549760>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7dca31549800>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7dca315498a0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7dca3167b240>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1000448, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1764162354098349835, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlgAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAADtWBU+j3g8PyI5vDzaT6W+u+SrPbbpS70AAAAAAAAAAJSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJLAUsIhpSMAUOUdJRSlC4="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWAQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksBhZSMAUOUdJRSlC4="}, "_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", "_shape": [8], "low": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]", "bounded_below": "[ True True True True True True True True]", "high": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]", "bounded_above": "[ True True True True True True True True]", "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:": "gAWV3AAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwFZHR5cGWUjAVudW1weZSMBWR0eXBllJOUjAJpOJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRijAFulIwWbnVtcHkuX2NvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlGgLQwgEAAAAAAAAAJSGlFKUjAVzdGFydJRoEWgLQwgAAAAAAAAAAJSGlFKUjAZfc2hhcGWUKYwKX25wX3JhbmRvbZROdWIu", "dtype": "int64", "n": "4", "start": "0", "_shape": [], "_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-6.6.105+-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": "1.2.2", "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:d68710a11640e04f260e787235846876b212fe4525d34a6b6e639fbde5c54917
|
| 3 |
+
size 148484
|
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 0x7dca315491c0>",
|
| 8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7dca31549260>",
|
| 9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7dca31549300>",
|
| 10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7dca315493a0>",
|
| 11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7dca31549440>",
|
| 12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7dca315494e0>",
|
| 13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7dca31549580>",
|
| 14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7dca31549620>",
|
| 15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7dca315496c0>",
|
| 16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7dca31549760>",
|
| 17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7dca31549800>",
|
| 18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7dca315498a0>",
|
| 19 |
+
"__abstractmethods__": "frozenset()",
|
| 20 |
+
"_abc_impl": "<_abc._abc_data object at 0x7dca3167b240>"
|
| 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": 1764162354098349835,
|
| 30 |
+
"learning_rate": 0.0003,
|
| 31 |
+
"tensorboard_log": null,
|
| 32 |
+
"_last_obs": {
|
| 33 |
+
":type:": "<class 'numpy.ndarray'>",
|
| 34 |
+
":serialized:": "gAWVlgAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAADtWBU+j3g8PyI5vDzaT6W+u+SrPbbpS70AAAAAAAAAAJSMBW51bXB5lIwFZHR5cGWUk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJLAUsIhpSMAUOUdJRSlC4="
|
| 35 |
+
},
|
| 36 |
+
"_last_episode_starts": {
|
| 37 |
+
":type:": "<class 'numpy.ndarray'>",
|
| 38 |
+
":serialized:": "gAWVdQAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWAQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksBhZSMAUOUdJRSlC4="
|
| 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": 3908,
|
| 55 |
+
"observation_space": {
|
| 56 |
+
":type:": "<class 'gymnasium.spaces.box.Box'>",
|
| 57 |
+
":serialized:": "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",
|
| 58 |
+
"dtype": "float32",
|
| 59 |
+
"_shape": [
|
| 60 |
+
8
|
| 61 |
+
],
|
| 62 |
+
"low": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]",
|
| 63 |
+
"bounded_below": "[ True True True True True True True True]",
|
| 64 |
+
"high": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]",
|
| 65 |
+
"bounded_above": "[ True True True True True True True True]",
|
| 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:": "gAWV3AAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwFZHR5cGWUjAVudW1weZSMBWR0eXBllJOUjAJpOJSJiIeUUpQoSwOMATyUTk5OSv////9K/////0sAdJRijAFulIwWbnVtcHkuX2NvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlGgLQwgEAAAAAAAAAJSGlFKUjAVzdGFydJRoEWgLQwgAAAAAAAAAAJSGlFKUjAZfc2hhcGWUKYwKX25wX3JhbmRvbZROdWIu",
|
| 73 |
+
"dtype": "int64",
|
| 74 |
+
"n": "4",
|
| 75 |
+
"start": "0",
|
| 76 |
+
"_shape": [],
|
| 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:": "gAWV1gIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwiVAZcAiQFTAJROhZQpjAFflIWUjEkvdXNyL2xvY2FsL2xpYi9weXRob24zLjEyL2Rpc3QtcGFja2FnZXMvc3RhYmxlX2Jhc2VsaW5lczMvY29tbW9uL3V0aWxzLnB5lIwEZnVuY5SMGWNvbnN0YW50X2ZuLjxsb2NhbHM+LmZ1bmOUS4RDCPiAANgPEogKlEMAlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSS91c3IvbG9jYWwvbGliL3B5dGhvbjMuMTIvZGlzdC1wYWNrYWdlcy9zdGFibGVfYmFzZWxpbmVzMy9jb21tb24vdXRpbHMucHmUdU5OaACMEF9tYWtlX2VtcHR5X2NlbGyUk5QpUpSFlHSUUpRoAIwSX2Z1bmN0aW9uX3NldHN0YXRllJOUaCF9lH2UKGgYjARmdW5jlIwMX19xdWFsbmFtZV9flIwZY29uc3RhbnRfZm4uPGxvY2Fscz4uZnVuY5SMD19fYW5ub3RhdGlvbnNfX5R9lIwOX19rd2RlZmF1bHRzX1+UTowMX19kZWZhdWx0c19flE6MCl9fbW9kdWxlX1+UaBmMB19fZG9jX1+UTowLX19jbG9zdXJlX1+UaACMCl9tYWtlX2NlbGyUk5RHPzOpKjBVMmGFlFKUhZSMF19jbG91ZHBpY2tsZV9zdWJtb2R1bGVzlF2UjAtfX2dsb2JhbHNfX5R9lHWGlIZSMC4="
|
| 98 |
+
}
|
| 99 |
+
}
|
ppo-LunarLander-v2/policy.optimizer.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4e8a53f0a06696323eacfdea2f3e46a6e1e3ffb7a54f0daa5db7db6a7d5ea0f7
|
| 3 |
+
size 88695
|
ppo-LunarLander-v2/policy.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:10986bc2d50c547fecd6ea6bcee63c0da9c9f85cc17b284b94eeb412f7afbeb4
|
| 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: 1.2.2
|
| 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:c033ad039bdf703068ba56e7395922b43bcf2fc986d128219395743772631dad
|
| 3 |
+
size 129780
|
results.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"mean_reward": 235.74914660000005, "std_reward": 63.14402667273864, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-11-26T14:29:38.244272"}
|