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: 252.42 +/- 15.47
|
| 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 0x7d1dd125fec0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d1dd125ff60>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d1dd1260040>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d1dd12600e0>", "_build": "<function ActorCriticPolicy._build at 0x7d1dd1260180>", "forward": "<function ActorCriticPolicy.forward at 0x7d1dd1260220>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7d1dd12602c0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d1dd1260360>", "_predict": "<function ActorCriticPolicy._predict at 0x7d1dd1260400>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d1dd12604a0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d1dd1260540>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7d1dd12605e0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7d1dd1b69780>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1000448, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1739516385077514967, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAJqTVTz2bLc/ICAnP53e9D4V5FK8KyTYvQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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:": "gAWVLgwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQG/RSM1jy4GMAWyUTQ4BjAF0lEdAnJo327FsHnV9lChoBkdAb2kbkwN9Y2gHTQUBaAhHQJyc3N8ma6V1fZQoaAZHQG5aFaSs8xNoB01JAWgIR0CcnrS/CZWrdX2UKGgGR0BwbiGrS3LFaAdNGgFoCEdAnKBO3DvVmXV9lChoBkdAcOvqSHM2WWgHTSYBaAhHQJyh9kiD/VB1fZQoaAZHQHFKlnmJWNpoB00CAWgIR0CcpImBOHnEdX2UKGgGR0Bxps+QlruZaAdNQwFoCEdAnKZTifg75nV9lChoBkdAa6O1x82Ji2gHTS8BaAhHQJyn94D9wWF1fZQoaAZHQHGKcxKxs2xoB00oAWgIR0CcqsuCwr1/dX2UKGgGR0Bv59OKwY+CaAdNDgFoCEdAnKxMK9f1H3V9lChoBkdAcXJadtl7MWgHTSkBaAhHQJyt6+bmU4d1fZQoaAZHQHB9it/4IrxoB00KAWgIR0Ccr2jHXEqEdX2UKGgGR0Bw9nbYbsF/aAdNAwFoCEdAnLH/24/eL3V9lChoBkdAbvDqTKT0QWgHTTcBaAhHQJyzvcmBvrJ1fZQoaAZHQGDyeyJKraNoB03oA2gIR0CcutcUuctodX2UKGgGR0Bxz2T9sJpnaAdL9WgIR0CcvJSJj2BbdX2UKGgGR0BuQlN8E3bVaAdNEAFoCEdAnL5o0ygwoXV9lChoBkdAcPX1vVEux2gHTXcBaAhHQJzCsbT+ee51fZQoaAZHQHKllL39JjFoB01UAWgIR0CcxJLOiWVvdX2UKGgGR0BxtzWwu/UOaAdNFAFoCEdAnMYT101ZT3V9lChoBkdAcFSlT3qRl2gHTR0BaAhHQJzI2xGDtgN1fZQoaAZHQHDjHDBMzuZoB01aAWgIR0CcysOH31zydX2UKGgGR0Bxq5KDkELZaAdNUQFoCEdAnMyYiTt9hXV9lChoBkdAcd2PIn0CimgHS/5oCEdAnM8WWY4Qz3V9lChoBkdAcD9B8QZn+WgHTeQBaAhHQJzRzpbD/ER1fZQoaAZHQHJDcqJ/G2loB003AWgIR0Cc045eJHiFdX2UKGgGR0Bwet3/xUedaAdNPAFoCEdAnNZ8ENe+mHV9lChoBkdAcuSV6NVBEGgHTQIBaAhHQJzX7eCTUy51fZQoaAZHQHAaLkbPyCpoB00+AWgIR0Cc2aj1wo9cdX2UKGgGR0BvCEL2HtWuaAdNEQFoCEdAnNxZP2wmmnV9lChoBkdAb3VTMqz7dmgHTQcBaAhHQJzdzQnhKlJ1fZQoaAZHQG1MLNfPX05oB00kAWgIR0Cc37A6+36RdX2UKGgGR0ButczCUHIIaAdNGwFoCEdAnOE4fbKzRnV9lChoBkdAbbtcIqsls2gHTR8BaAhHQJzj/nDBMzx1fZQoaAZHQHGIZsXSBshoB0v0aAhHQJzlT+T/yXl1fZQoaAZHQG/I7s4T9KpoB00QAWgIR0Cc5s2kBS1mdX2UKGgGR0Bx2ck+otL+aAdNLAFoCEdAnOh1ZPl+3HV9lChoBkdAcGbndfsu4GgHTQABaAhHQJzrcRujynV1fZQoaAZHQG3ouZLIxQBoB0vnaAhHQJztEzzmOlx1fZQoaAZHQFAZauOjqOdoB0vVaAhHQJzug33pOet1fZQoaAZHQHFgrIDHOr1oB01wAWgIR0Cc8rYTj/+9dX2UKGgGR0BvHAmqo60ZaAdNMAFoCEdAnPRj3M6ikHV9lChoBkdAcKTMX7+DOGgHTQkBaAhHQJz12Eh7mdR1fZQoaAZHQG/Bgam4y45oB01GAWgIR0Cc96LteD3/dX2UKGgGR0A6qDgqEvkBaAdLwmgIR0Cc+eTVUdaMdX2UKGgGR0BvsDVlPJq7aAdNTgFoCEdAnPu+9OARTXV9lChoBkdAbfwphnanJmgHTQMBaAhHQJz9MsnRb8p1fZQoaAZHQG4gFj3Ehq1oB00HAWgIR0Cc/p+Jxeb/dX2UKGgGR0BDxLTx5LRKaAdL+WgIR0CdATIxxkupdX2UKGgGR0Bt2Bfv4M4MaAdNMQFoCEdAnQLp8KG+K3V9lChoBkdAchNBguyu6mgHTVQBaAhHQJ0EynvUjLV1fZQoaAZHQEBEUWVNYbNoB0voaAhHQJ0HOe9SMtN1fZQoaAZHQHD2oOpbUw1oB00xAWgIR0CdCPKraM72dX2UKGgGR0ByWTjENvwWaAdNwAFoCEdAnQtyngpBonV9lChoBkdAcIUKiwjdHmgHS/xoCEdAnQzWO2iL23V9lChoBkdAbyRLRrrPdGgHTSsBaAhHQJ0PnAM2FWZ1fZQoaAZHQGy4r/CIk7hoB00gAWgIR0CdETWoFV1fdX2UKGgGR0Bw9Odc0LtvaAdNYQJoCEdAnRXLtE5QxnV9lChoBkdAb5YXcgyM1mgHTX8BaAhHQJ0X3tkWhyt1fZQoaAZHQHA33HBDXvpoB004AWgIR0CdGZnXd0q6dX2UKGgGR0Bww6dH2AXmaAdL92gIR0CdHOWLP2PDdX2UKGgGR0Bx0GyY5T60aAdL+WgIR0CdHqP07KaHdX2UKGgGR0BwV42XLNfPaAdNLAFoCEdAnSD6l+EytXV9lChoBkdAcR2/7BO58WgHTSsBaAhHQJ0i1prULD11fZQoaAZHQHBIjoQnQY1oB00TAWgIR0CdJZGZeAuqdX2UKGgGR0Bw+OD28IzFaAdNHQFoCEdAnScriVB2OnV9lChoBkdAcajU4JeE7GgHTSEBaAhHQJ0ou54GD+R1fZQoaAZHQHDaAOvt+kRoB002AWgIR0CdK6mA9V3mdX2UKGgGR0BwjJNi6QNkaAdNOAFoCEdAnS1f+jua4XV9lChoBkdAcWzstTUAk2gHTc8DaAhHQJ00AiY9gWt1fZQoaAZHQHBdoR28qWloB00xAWgIR0CdNapblijMdX2UKGgGR0BGD5IH1OCYaAdLymgIR0CdNswR5C4SdX2UKGgGR0BT1PPgNwzdaAdL12gIR0CdOSBmwqy4dX2UKGgGR0BwWzLZBcAzaAdNEwFoCEdAnTrJW/8EV3V9lChoBkdAcJM6AOJ+D2gHTSMBaAhHQJ08Xlmvnr91fZQoaAZHQG6bL2HtWuJoB00+AWgIR0CdPih2nsLOdX2UKGgGR0BwpZf/m1YyaAdNHgFoCEdAnUDmCNCJGnV9lChoBkdAcWCElVtGeGgHTS4BaAhHQJ1Cnp/wy7B1fZQoaAZHQG71jkELYwtoB01iAWgIR0CdRIyXD3uedX2UKGgGR0BzBr8jzI3jaAdNZAFoCEdAnUe8Pe54GHV9lChoBkdAU2fKHO8kEGgHS+loCEdAnUj7iVB2OnV9lChoBkdAcb2pS75EdGgHTRsBaAhHQJ1K9PM0P6N1fZQoaAZHQG/INLL6k69oB00NAWgIR0CdTNeoDPnkdX2UKGgGR0Bw2Uz544ZNaAdL62gIR0CdUBkI5YHPdX2UKGgGR0BwNcqI7/4qaAdNHgFoCEdAnVIVJ17pmnV9lChoBkdAYxnc/t6X0GgHTegDaAhHQJ1Y6XY150N1fZQoaAZHQG9yj6vaDf5oB01SAWgIR0CdWsYqXnhbdX2UKGgGR0BwZyhufmLcaAdNEAFoCEdAnVxe2d/ax3V9lChoBkdAcNt9kSVW0mgHTQ8BaAhHQJ1fHWGyon91fZQoaAZHQE8H0QK8cuJoB0vzaAhHQJ1gdPGhmGx1fZQoaAZHQHJ17b1yvLZoB00XAWgIR0CdYgXnhbW3dX2UKGgGR0BxsjWSU1Q7aAdL/WgIR0CdZJoIOYpldX2UKGgGR0A4W7hvR7Z4aAdLumgIR0CdZaTgVGkOdX2UKGgGR0Bwr55rxiG4aAdNAgFoCEdAnWcaV6eGwnV9lChoBkdARD1W+49X92gHS/poCEdAnWiB0hePaXV9lChoBkdATJ1qzqrzXmgHS+BoCEdAnWnH/Pw/gXV9lChoBkdAcJXWHk92YGgHTT4BaAhHQJ1swYaYNRZ1fZQoaAZHQHCrQ9A5aNdoB0v3aAhHQJ1uIV32VVx1fZQoaAZHQHEZePRzBARoB00zAWgIR0Cdb9ivPkaNdX2UKGgGR0BuzPokiUxEaAdL/mgIR0CdcTYbKifydWUu"}, "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": "Generator(PCG64)"}, "action_space": {":type:": "<class 'gymnasium.spaces.discrete.Discrete'>", ":serialized:": "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", "n": "4", "start": "0", "_shape": [], "dtype": "int64", "_np_random": "Generator(PCG64)"}, "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-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024", "Python": "3.11.11", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.5.1+cu124", "GPU Enabled": "True", "Numpy": "1.26.4", "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 sha256:440c5c92ae5327edefb4678e4b8e0d3d4290f3c6fd8428787a8ec74789622866
|
| 3 |
+
size 148006
|
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 0x7d1dd125fec0>",
|
| 8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7d1dd125ff60>",
|
| 9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7d1dd1260040>",
|
| 10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7d1dd12600e0>",
|
| 11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7d1dd1260180>",
|
| 12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7d1dd1260220>",
|
| 13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7d1dd12602c0>",
|
| 14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7d1dd1260360>",
|
| 15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7d1dd1260400>",
|
| 16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7d1dd12604a0>",
|
| 17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7d1dd1260540>",
|
| 18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7d1dd12605e0>",
|
| 19 |
+
"__abstractmethods__": "frozenset()",
|
| 20 |
+
"_abc_impl": "<_abc._abc_data object at 0x7d1dd1b69780>"
|
| 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": 1739516385077514967,
|
| 30 |
+
"learning_rate": 0.0003,
|
| 31 |
+
"tensorboard_log": null,
|
| 32 |
+
"_last_obs": {
|
| 33 |
+
":type:": "<class 'numpy.ndarray'>",
|
| 34 |
+
":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAJqTVTz2bLc/ICAnP53e9D4V5FK8KyTYvQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": 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": "Generator(PCG64)"
|
| 69 |
+
},
|
| 70 |
+
"action_space": {
|
| 71 |
+
":type:": "<class 'gymnasium.spaces.discrete.Discrete'>",
|
| 72 |
+
":serialized:": "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",
|
| 73 |
+
"n": "4",
|
| 74 |
+
"start": "0",
|
| 75 |
+
"_shape": [],
|
| 76 |
+
"dtype": "int64",
|
| 77 |
+
"_np_random": "Generator(PCG64)"
|
| 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:b4f75ca89656927f81226611a921f21beb0437d0c396a3af433e341766d0ba38
|
| 3 |
+
size 88362
|
ppo-LunarLander-v2/policy.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:9f5e5ca9037c367ef87bdb4bb1c87a3d9d8e198ec6f1947c681a227955e86e25
|
| 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-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024
|
| 2 |
+
- Python: 3.11.11
|
| 3 |
+
- Stable-Baselines3: 2.0.0a5
|
| 4 |
+
- PyTorch: 2.5.1+cu124
|
| 5 |
+
- GPU Enabled: True
|
| 6 |
+
- Numpy: 1.26.4
|
| 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:b6af4ea3d5dc61bc8795b914e2a42c9c7f049b38b6ee8c65b6dce1f7f65f747b
|
| 3 |
+
size 164051
|
results.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"mean_reward": 252.4207898474274, "std_reward": 15.466336518234144, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-02-14T08:28:40.697924"}
|