First commit on hf
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 +96 -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: 201.09 +/- 92.19
|
| 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 0x7932289ed800>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7932289ed8a0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7932289ed940>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7932289ed9e0>", "_build": "<function ActorCriticPolicy._build at 0x7932289eda80>", "forward": "<function ActorCriticPolicy.forward at 0x7932289edb20>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7932289edbc0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7932289edc60>", "_predict": "<function ActorCriticPolicy._predict at 0x7932289edd00>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7932289edda0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7932289ede40>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7932289edee0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x793228951940>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1747096465484094349, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": null, "_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:": "gAWVMAwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQG+Vkxyn1nOMAWyUTWUBjAF0lEdAnd1xhYvFnHV9lChoBkdAcSnPO6d1+2gHTR0BaAhHQJ3dkaIeo1l1fZQoaAZHQDd5eC04R29oB00DAWgIR0Cd4UuQ6p5vdX2UKGgGR0BwyrjyWiUQaAdNJAFoCEdAneK0C/47BHV9lChoBkdAcMD5u63AmGgHTR4BaAhHQJ3iwUO/cnF1fZQoaAZHQGoGtUXHim5oB008AWgIR0Cd46o3Jgb7dX2UKGgGR0BwKmgpSaVlaAdL/2gIR0Cd5RexOclPdX2UKGgGR0BtIyowVTJhaAdNUAFoCEdAneV9K28Zk3V9lChoBkdAbnE5aNdZ72gHTXQBaAhHQJ3mYA1ejVR1fZQoaAZHQG/rELQXyiFoB00jAWgIR0Cd5tYoy9EkdX2UKGgGR0Bu7olyBClaaAdNEQFoCEdAneccGPgeinV9lChoBkdAbJ5Dpkf9xmgHTVkBaAhHQJ3qVz3h4t91fZQoaAZHQEeVedCmdiFoB0vRaAhHQJ3ruV3Ux211fZQoaAZHQGnyEBKcurZoB0v9aAhHQJ3sLvBrN4Z1fZQoaAZHQGOquLrHEMtoB03oA2gIR0Cd7JrqdH2AdX2UKGgGR0Bs0AQL/jsEaAdNuwFoCEdAnlCvikwevXV9lChoBkdAXznIJZ4fOmgHTegDaAhHQJ5RQZ1mrbR1fZQoaAZHQHDLNAs052hoB00vAWgIR0CeUZLqUu+RdX2UKGgGR0BwiozFdcB2aAdL/WgIR0CeUtUaya/idX2UKGgGR0BxPYS26TW5aAdNDwFoCEdAnle5N47ihnV9lChoBkdAYT1Kujh1kmgHTegDaAhHQJ5ZJYRujyp1fZQoaAZHQHEWF2NedCpoB0v9aAhHQJ5Z0kNWluZ1fZQoaAZHQG5fPUz9CNVoB00ZAWgIR0CeWlCHh0hedX2UKGgGR0Bv/yg7HQyAaAdNEAFoCEdAnlphN/OMVHV9lChoBkdAbeDch1Tzd2gHTUwCaAhHQJ5f/T+ee4F1fZQoaAZHQHI/A5Jbt7doB0v5aAhHQJ5gyx3V0911fZQoaAZHQGuFDMFEAo5oB007AmgIR0CeYbpFkQPJdX2UKGgGR0BuCyg7HQyAaAdNOgFoCEdAnmI9pmEoOXV9lChoBkdAb9gf6GgzxmgHTWUBaAhHQJ5jL3Dej211fZQoaAZHQEkgTC+De0poB0vnaAhHQJ5lBp35eqt1fZQoaAZHQHJggKWszVNoB0v/aAhHQJ5lGwHJLdx1fZQoaAZHQG5KJL26ClJoB01vAmgIR0CeZaesgdOqdX2UKGgGR0BssZbSqlxfaAdNFAFoCEdAnmc25c1O03V9lChoBkdAbGc0NSZSemgHTbkCaAhHQJ5nTL9uP3l1fZQoaAZHQF44dk8RtgtoB03oA2gIR0CeZ3mnO0LMdX2UKGgGR0BhshGrjo6kaAdN6ANoCEdAnmrRQ79ycXV9lChoBkdAbzjTSb6P82gHTQ8BaAhHQJ5rjxDst051fZQoaAZHQHHOxKDkELZoB00/AmgIR0CebC3VTaTPdX2UKGgGR0Bvgd3pwCKaaAdNFwFoCEdAnm4FLFn7HnV9lChoBkdAbHYHyEtdzGgHTTwBaAhHQJ5uIPXkHUt1fZQoaAZHQG7xqcEvCdloB0v8aAhHQJ5upZV4oql1fZQoaAZHQG3K61TisGRoB00IAWgIR0Ceb61hsqJ/dX2UKGgGR0Bwlft/nW8RaAdNHQFoCEdAnm/sGcFyJnV9lChoBkdAbg3+gDifhGgHTR0BaAhHQJ5yINtqHoJ1fZQoaAZHQGgZyp71Iy1oB002AWgIR0CecsmReTmodX2UKGgGR0BgocVvddmhaAdN6ANoCEdAnnNoMF2V3XV9lChoBkdAb+jbFjurqGgHS/hoCEdAnnQMTFl05nV9lChoBkdAceg2Xb/OuGgHTawCaAhHQJ52yaKDTSd1fZQoaAZHQHHghhpg1FZoB0v/aAhHQJ53pSDRMOB1fZQoaAZHQHAmbf1pTMtoB000AWgIR0Ced7zZ6D5CdX2UKGgGR0BvvemR/3FlaAdNFwFoCEdAnnh3Hq/ucHV9lChoBkdAcAIwW3z+WGgHS/1oCEdAnnk6FuejEnV9lChoBkdAcF5LeyiVSmgHTWwBaAhHQJ55U3Kji4t1fZQoaAZHQDSgc6vJRwZoB0u/aAhHQJ55+wnpjc51fZQoaAZHQG+GApKBd2RoB016AmgIR0CeemAGB4D+dX2UKGgGR0Bs1WqkuYhMaAdNLwFoCEdAnnsMpw0fo3V9lChoBkdAbFfgl4TsY2gHTT0CaAhHQJ58YSg5BC51fZQoaAZHQG7Oq+rU9ZBoB00WAWgIR0CefWd4FA3UdX2UKGgGR0BEWqpcX3xnaAdNDwFoCEdAnn29R3u/lHV9lChoBkdAcPpjOLR8dGgHTRIBaAhHQJ6AO7yxzJZ1fZQoaAZHQG5L28h9srNoB00BAWgIR0CegF/GEPDpdX2UKGgGR0BgAcDW9US7aAdN6ANoCEdAnoG9uP3i73V9lChoBkdAbYSxtYSxq2gHTREBaAhHQJ6Bvo+wC8x1fZQoaAZHQG/CZGax5cFoB00RAWgIR0CegmNjbzshdX2UKGgGR0BvTln7HhjwaAdNEAFoCEdAnoJygPEsKHV9lChoBkdAazaWqLjxTmgHTQ0BaAhHQJ6DbGZNO/N1fZQoaAZHQG/6aRp1zQxoB00ZAWgIR0CehJzRx95RdX2UKGgGR0BVyvxQSBbwaAdN6ANoCEdAnoUSzC1qnHV9lChoBkdASH7uOS4e92gHS/RoCEdAnoYcKG+K0nV9lChoBkdAcTY/1QIldGgHTRwBaAhHQJ6HHsZ5zHV1fZQoaAZHQEapdMTN+spoB0vPaAhHQJ6HygyuZCx1fZQoaAZHQHDoEPhAGB5oB0v3aAhHQJ6JkiosI3R1fZQoaAZHQHDgj7l7tzFoB032AWgIR0CeicXKbKA8dX2UKGgGR0BwDuWD6FdtaAdNgQFoCEdAnoqgN9YwI3V9lChoBkdAcL5JFb3XZ2gHTQYBaAhHQJ6MQlByCFt1fZQoaAZHQHB0W/WUbDNoB00IAWgIR0CejFwKSgXedX2UKGgGR0BsGmaBqbjMaAdNBwFoCEdAno1Ape/pMnV9lChoBkdAcF5MLncL0GgHTRYBaAhHQJ6N6XzDn/11fZQoaAZHQGk3dyLhrFhoB00WAWgIR0Cej2tgKF7EdX2UKGgGR0BuurWsijcmaAdNCQFoCEdAnpIUfgaWHHV9lChoBkdANZnU+cH4XWgHS/loCEdAnpKRbwBo3HV9lChoBkdAQ1QJ7b+LnGgHS9loCEdAnpQOmR/3FnV9lChoBkdAK6J+tr9ETmgHS/doCEdAnpRion8baXV9lChoBkdAY0rjkMkQgGgHTegDaAhHQJ6U2NBF/hF1fZQoaAZHQGyTOrp7kXFoB01CAWgIR0CelfrFfiPydX2UKGgGR0BxUzKdQO4HaAdNKgFoCEdAnpaYy44IbHV9lChoBkdAcViP+GXXy2gHTQsBaAhHQJ6XWrWAf+11fZQoaAZHQGPlkRzzVc5oB03oA2gIR0CemHOfNA1OdX2UKGgGR0Bxz0gieNDMaAdNOAFoCEdAnpj6ab4Ju3V9lChoBkdAbpi9QGfPHGgHTTUBaAhHQJ6aAfHPu5V1fZQoaAZHQGjlJu/DcdpoB00hAWgIR0CemmsPJ7swdX2UKGgGR0BsPb70nPVvaAdNJAFoCEdAnpzxaX8fm3V9lChoBkdAcMBdXko4MmgHTQEBaAhHQJ6dnQpnYg91fZQoaAZHQHFZhHTZxrBoB010AmgIR0CenowIdELIdX2UKGgGR0BwQ1KbrkbQaAdNHwFoCEdAnp8uEdvKl3V9lChoBkdAcKQDGLk0amgHS/doCEdAnp/FOXVslHV9lChoBkdAcPdhxo7FKmgHTSgBaAhHQJ6f/7hvR7Z1fZQoaAZHQF7brWy1NQFoB03oA2gIR0CeoEqy4Wk8dX2UKGgGR0BuTOTFERapaAdNJAFoCEdAnqDDDwYtQXV9lChoBkdAbDozj3mFJ2gHTQUBaAhHQJ6g8slLOA11ZS4="}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 310, "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:": "gAWV/gAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFm51bXB5Ll9jb3JlLm11bHRpYXJyYXmUjAZzY2FsYXKUk5SMBW51bXB5lIwFZHR5cGWUk5SMAmk4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJDCAQAAAAAAAAAlIaUUpSMBXN0YXJ0lGgIaA5DCAAAAAAAAAAAlIaUUpSMBl9zaGFwZZQpjAVkdHlwZZRoC4wCaTiUiYiHlFKUKEsDaA9OTk5K/////0r/////SwB0lGKMCl9ucF9yYW5kb22UTnViLg==", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 1, "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-6.1.123+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Mar 30 16:01:29 UTC 2025", "Python": "3.11.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.6.0+cu124", "GPU Enabled": "True", "Numpy": "2.0.2", "Cloudpickle": "3.1.1", "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:cfeef5d2dd12866c452e14a4791bef817b83c6ecbb39866cea1a7086451913fd
|
| 3 |
+
size 147351
|
ppo-LunarLander-v2/_stable_baselines3_version
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
2.0.0a5
|
ppo-LunarLander-v2/data
ADDED
|
@@ -0,0 +1,96 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 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 0x7932289ed800>",
|
| 8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7932289ed8a0>",
|
| 9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7932289ed940>",
|
| 10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7932289ed9e0>",
|
| 11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7932289eda80>",
|
| 12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7932289edb20>",
|
| 13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7932289edbc0>",
|
| 14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7932289edc60>",
|
| 15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7932289edd00>",
|
| 16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7932289edda0>",
|
| 17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7932289ede40>",
|
| 18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7932289edee0>",
|
| 19 |
+
"__abstractmethods__": "frozenset()",
|
| 20 |
+
"_abc_impl": "<_abc._abc_data object at 0x793228951940>"
|
| 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": 1747096465484094349,
|
| 30 |
+
"learning_rate": 0.0003,
|
| 31 |
+
"tensorboard_log": null,
|
| 32 |
+
"_last_obs": null,
|
| 33 |
+
"_last_episode_starts": {
|
| 34 |
+
":type:": "<class 'numpy.ndarray'>",
|
| 35 |
+
":serialized:": "gAWVhAAAAAAAAACME251bXB5Ll9jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksQhZSMAUOUdJRSlC4="
|
| 36 |
+
},
|
| 37 |
+
"_last_original_obs": null,
|
| 38 |
+
"_episode_num": 0,
|
| 39 |
+
"use_sde": false,
|
| 40 |
+
"sde_sample_freq": -1,
|
| 41 |
+
"_current_progress_remaining": -0.015808000000000044,
|
| 42 |
+
"_stats_window_size": 100,
|
| 43 |
+
"ep_info_buffer": {
|
| 44 |
+
":type:": "<class 'collections.deque'>",
|
| 45 |
+
":serialized:": "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"
|
| 46 |
+
},
|
| 47 |
+
"ep_success_buffer": {
|
| 48 |
+
":type:": "<class 'collections.deque'>",
|
| 49 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
| 50 |
+
},
|
| 51 |
+
"_n_updates": 310,
|
| 52 |
+
"observation_space": {
|
| 53 |
+
":type:": "<class 'gymnasium.spaces.box.Box'>",
|
| 54 |
+
":serialized:": "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",
|
| 55 |
+
"dtype": "float32",
|
| 56 |
+
"bounded_below": "[ True True True True True True True True]",
|
| 57 |
+
"bounded_above": "[ True True True True True True True True]",
|
| 58 |
+
"_shape": [
|
| 59 |
+
8
|
| 60 |
+
],
|
| 61 |
+
"low": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]",
|
| 62 |
+
"high": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]",
|
| 63 |
+
"low_repr": "[-90. -90. -5. -5. -3.1415927 -5.\n -0. -0. ]",
|
| 64 |
+
"high_repr": "[90. 90. 5. 5. 3.1415927 5.\n 1. 1. ]",
|
| 65 |
+
"_np_random": null
|
| 66 |
+
},
|
| 67 |
+
"action_space": {
|
| 68 |
+
":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
|
| 69 |
+
":serialized:": "gAWV/gAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFm51bXB5Ll9jb3JlLm11bHRpYXJyYXmUjAZzY2FsYXKUk5SMBW51bXB5lIwFZHR5cGWUk5SMAmk4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGJDCAQAAAAAAAAAlIaUUpSMBXN0YXJ0lGgIaA5DCAAAAAAAAAAAlIaUUpSMBl9zaGFwZZQpjAVkdHlwZZRoC4wCaTiUiYiHlFKUKEsDaA9OTk5K/////0r/////SwB0lGKMCl9ucF9yYW5kb22UTnViLg==",
|
| 70 |
+
"n": "4",
|
| 71 |
+
"start": "0",
|
| 72 |
+
"_shape": [],
|
| 73 |
+
"dtype": "int64",
|
| 74 |
+
"_np_random": null
|
| 75 |
+
},
|
| 76 |
+
"n_envs": 1,
|
| 77 |
+
"n_steps": 2048,
|
| 78 |
+
"gamma": 0.99,
|
| 79 |
+
"gae_lambda": 0.95,
|
| 80 |
+
"ent_coef": 0.0,
|
| 81 |
+
"vf_coef": 0.5,
|
| 82 |
+
"max_grad_norm": 0.5,
|
| 83 |
+
"batch_size": 64,
|
| 84 |
+
"n_epochs": 10,
|
| 85 |
+
"clip_range": {
|
| 86 |
+
":type:": "<class 'function'>",
|
| 87 |
+
":serialized:": "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"
|
| 88 |
+
},
|
| 89 |
+
"clip_range_vf": null,
|
| 90 |
+
"normalize_advantage": true,
|
| 91 |
+
"target_kl": null,
|
| 92 |
+
"lr_schedule": {
|
| 93 |
+
":type:": "<class 'function'>",
|
| 94 |
+
":serialized:": "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"
|
| 95 |
+
}
|
| 96 |
+
}
|
ppo-LunarLander-v2/policy.optimizer.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1652ae70c4d8336b00a1712c1d72a5102012dcf2873dca90ec5903b3bc7ef87d
|
| 3 |
+
size 88490
|
ppo-LunarLander-v2/policy.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:f4e8ae1598c0a05d67d10334598ba4868b688830231bb7d2c5468a054eab8960
|
| 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-6.1.123+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Sun Mar 30 16:01:29 UTC 2025
|
| 2 |
+
- Python: 3.11.12
|
| 3 |
+
- Stable-Baselines3: 2.0.0a5
|
| 4 |
+
- PyTorch: 2.6.0+cu124
|
| 5 |
+
- GPU Enabled: True
|
| 6 |
+
- Numpy: 2.0.2
|
| 7 |
+
- Cloudpickle: 3.1.1
|
| 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 sha256:7235e9275639c712eb7295e78abf93a75dbd8dca4c99b9e5c12dc290a7c8090f
|
| 3 |
+
size 158382
|
results.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"mean_reward": 201.0924952, "std_reward": 92.19046777123393, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-05-13T01:31:39.300919"}
|