Upload PPO LunarLander-v2 trained agent with 2M timesteps
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: 272.98 +/- 20.86
|
| 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 0x7e596cb71580>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e596cb71620>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e596cb716c0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7e596cb71760>", "_build": "<function ActorCriticPolicy._build at 0x7e596cb71800>", "forward": "<function ActorCriticPolicy.forward at 0x7e596cb718a0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7e596cb71940>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7e596cb719e0>", "_predict": "<function ActorCriticPolicy._predict at 0x7e596cb71a80>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7e596cb71b20>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7e596cb71bc0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7e596cb71c60>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7e596cff43c0>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 2031616, "_total_timesteps": 2000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1754146524839948774, "learning_rate": 0.00025, "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": 620, "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:": "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": 0.01, "vf_coef": 0.5, "max_grad_norm": 0.5, "batch_size": 128, "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": "3.11.13", "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 sha256:674ebc6c1e29ad869663eeedd1af4fd9eca6c9c3209d03885c3b1e3168e840f2
|
| 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 0x7e596cb71580>",
|
| 8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7e596cb71620>",
|
| 9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7e596cb716c0>",
|
| 10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7e596cb71760>",
|
| 11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7e596cb71800>",
|
| 12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7e596cb718a0>",
|
| 13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7e596cb71940>",
|
| 14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7e596cb719e0>",
|
| 15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7e596cb71a80>",
|
| 16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7e596cb71b20>",
|
| 17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7e596cb71bc0>",
|
| 18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7e596cb71c60>",
|
| 19 |
+
"__abstractmethods__": "frozenset()",
|
| 20 |
+
"_abc_impl": "<_abc._abc_data object at 0x7e596cff43c0>"
|
| 21 |
+
},
|
| 22 |
+
"verbose": 1,
|
| 23 |
+
"policy_kwargs": {},
|
| 24 |
+
"num_timesteps": 2031616,
|
| 25 |
+
"_total_timesteps": 2000000,
|
| 26 |
+
"_num_timesteps_at_start": 0,
|
| 27 |
+
"seed": null,
|
| 28 |
+
"action_noise": null,
|
| 29 |
+
"start_time": 1754146524839948774,
|
| 30 |
+
"learning_rate": 0.00025,
|
| 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:": "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"
|
| 49 |
+
},
|
| 50 |
+
"ep_success_buffer": {
|
| 51 |
+
":type:": "<class 'collections.deque'>",
|
| 52 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
| 53 |
+
},
|
| 54 |
+
"_n_updates": 620,
|
| 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": 0.01,
|
| 84 |
+
"vf_coef": 0.5,
|
| 85 |
+
"max_grad_norm": 0.5,
|
| 86 |
+
"batch_size": 128,
|
| 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 sha256:94adc7d7c8882b06f83e9b8384f3625fb55d8461f913f47429cd1ea1fa935c9b
|
| 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:a741f46bc611588ddf1ae231dfd60c2d65f3f00bf68313266edc27c670b12870
|
| 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: 3.11.13
|
| 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:c6dd100f2d99d94a5a0e1348fc5eb798bd8658e5cf3a2a0b9e9dd422ff843c26
|
| 3 |
+
size 149589
|
results.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"mean_reward": 272.9810253, "std_reward": 20.859393304802875, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2025-08-02T15:40:19.212375"}
|