Upload PPO LunarLander-v2 trained agent
Browse files- 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 +0 -0
- results.json +1 -0
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: 247.45 +/- 86.27
|
| 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 0x7ff895819870>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff895819900>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff895819990>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff895819a20>", "_build": "<function ActorCriticPolicy._build at 0x7ff895819ab0>", "forward": "<function ActorCriticPolicy.forward at 0x7ff895819b40>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff895819bd0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff895819c60>", "_predict": "<function ActorCriticPolicy._predict at 0x7ff895819cf0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff895819d80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff895819e10>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff895819ea0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7ff8957c1d00>"}, "verbose": 1, "policy_kwargs": {}, "num_timesteps": 1001472, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1733730786425768671, "learning_rate": 0.0003, "tensorboard_log": null, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAHoPFj4gHdE+yEddvdRws74YNHU9NcALvQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": -0.0014719999999999178, "_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": 4890, "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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=", "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-6.1.85+-x86_64-with-glibc2.35 # 1 SMP PREEMPT_DYNAMIC Thu Jun 27 21:05:47 UTC 2024", "Python": "3.10.12", "Stable-Baselines3": "2.0.0a5", "PyTorch": "2.5.1+cu121", "GPU Enabled": "True", "Numpy": "1.26.4", "Cloudpickle": "3.1.0", "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 sha256:8bc4c8480e49c5e1996a06a419fac672f2d9d700ea0ed94e972211fbc2b7946d
|
| 3 |
+
size 147252
|
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 0x7ff895819870>",
|
| 8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7ff895819900>",
|
| 9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7ff895819990>",
|
| 10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7ff895819a20>",
|
| 11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7ff895819ab0>",
|
| 12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7ff895819b40>",
|
| 13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7ff895819bd0>",
|
| 14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7ff895819c60>",
|
| 15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7ff895819cf0>",
|
| 16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7ff895819d80>",
|
| 17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7ff895819e10>",
|
| 18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7ff895819ea0>",
|
| 19 |
+
"__abstractmethods__": "frozenset()",
|
| 20 |
+
"_abc_impl": "<_abc._abc_data object at 0x7ff8957c1d00>"
|
| 21 |
+
},
|
| 22 |
+
"verbose": 1,
|
| 23 |
+
"policy_kwargs": {},
|
| 24 |
+
"num_timesteps": 1001472,
|
| 25 |
+
"_total_timesteps": 1000000,
|
| 26 |
+
"_num_timesteps_at_start": 0,
|
| 27 |
+
"seed": null,
|
| 28 |
+
"action_noise": null,
|
| 29 |
+
"start_time": 1733730786425768671,
|
| 30 |
+
"learning_rate": 0.0003,
|
| 31 |
+
"tensorboard_log": null,
|
| 32 |
+
"_last_obs": {
|
| 33 |
+
":type:": "<class 'numpy.ndarray'>",
|
| 34 |
+
":serialized:": "gAWVlQAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYgAAAAAAAAAHoPFj4gHdE+yEddvdRws74YNHU9NcALvQAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////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": -0.0014719999999999178,
|
| 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": 4890,
|
| 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:": "gAWV2wAAAAAAAACMGWd5bW5hc2l1bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpSMFW51bXB5LmNvcmUubXVsdGlhcnJheZSMBnNjYWxhcpSTlIwFbnVtcHmUjAVkdHlwZZSTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYkMIBAAAAAAAAACUhpRSlIwFc3RhcnSUaAhoDkMIAAAAAAAAAACUhpRSlIwGX3NoYXBllCmMBWR0eXBllGgOjApfbnBfcmFuZG9tlE51Yi4=",
|
| 73 |
+
"n": "4",
|
| 74 |
+
"start": "0",
|
| 75 |
+
"_shape": [],
|
| 76 |
+
"dtype": "int64",
|
| 77 |
+
"_np_random": null
|
| 78 |
+
},
|
| 79 |
+
"n_envs": 1,
|
| 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 sha256:1469a396b9a8edfb9efe84f3efcff73ea8573fc6fbae6a72ff7dae9853573892
|
| 3 |
+
size 88362
|
ppo-LunarLander-v2/policy.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1531fd70fa5af1c372bf37fc9d9bf8cb0621145c2a774cc9df624620c4baa91e
|
| 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: 3.10.12
|
| 3 |
+
- Stable-Baselines3: 2.0.0a5
|
| 4 |
+
- PyTorch: 2.5.1+cu121
|
| 5 |
+
- GPU Enabled: True
|
| 6 |
+
- Numpy: 1.26.4
|
| 7 |
+
- Cloudpickle: 3.1.0
|
| 8 |
+
- Gymnasium: 0.28.1
|
| 9 |
+
- OpenAI Gym: 0.25.2
|
replay.mp4
ADDED
|
Binary file (163 kB). View file
|
|
|
results.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"mean_reward": 247.4482594, "std_reward": 86.27216838683827, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2024-12-09T08:32:07.975413"}
|