model unit 1 upload
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 +94 -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 +7 -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: 266.68 +/- 18.95
|
| 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 sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\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 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 0x7f4b32a4c790>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4b32a4c820>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4b32a4c8b0>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4b32a4c940>", "_build": "<function ActorCriticPolicy._build at 0x7f4b32a4c9d0>", "forward": "<function ActorCriticPolicy.forward at 0x7f4b32a4ca60>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4b32a4caf0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f4b32a4cb80>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4b32a4cc10>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4b32a4cca0>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4b32a4cd30>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f4b32a42ed0>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "_shape": [8], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf]", "bounded_below": "[False False False False False False False False]", "bounded_above": "[False False False False False False False False]", "_np_random": null}, "action_space": {":type:": "<class 'gym.spaces.discrete.Discrete'>", ":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu", "n": 4, "_shape": [], "dtype": "int64", "_np_random": null}, "n_envs": 16, "num_timesteps": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1670939936403238818, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAOY1Ub24Jte5kBGftJ9k0a932j62aiFVMwAAgD8AAIA/8Bt1vhX5bD9L2ls+DJvDvgHgKL1GMuk9AAAAAAAAAADNWLK7e1Kruu4vcTUVQxswZ1WKugdBsbQAAIA/AACAPzZohj6LrSI/pXVNvvCBj765+1w90NnSvAAAAAAAAAAAmlsgvfaYW7pOiE4zERSzr0ul5bpwLL2zAACAPwAAgD+bNou+wptkP9pQH74jDrq+GgaXvmDb3j0AAAAAAAAAALOK1L3UOXk+1vFkPV/1fb45rRw7+9NitwAAAAAAAAAA5kABPeFClDl/VAY7i7ArPcLfVDuRLxg7AACAPwAAgD+mHxa+oF79Pko9UT1rw4i+irxZO/FBrz0AAAAAAAAAAGZXxrzCyr8/RklsvruFiT6JaQq9YnfLvQAAAAAAAAAA5pbAvYCnpz+oMs++i0eYvuNR/r3LOou+AAAAAAAAAAAA/Oq7a/ewPdhpur3M5oW+CH79vErYkrwAAAAAAAAAAIDrL71s6rW77dhqObYhxDyXRAy9A+2jPQAAgD8AAIA/msFhvVzxdLxiHAY6DNK7PEC/270q2pU9AACAPwAAgD9mKGS99hxWumJ5d7RnxCWwoYQLOsZhpTMAAIA/AACAPxqD0T1wvYw+se49vrSbib53SdQ55qsNvAAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="}, "_last_original_obs": null, "_episode_num": 0, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": -0.015808000000000044, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 248, "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, "system_info": {"OS": "Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022", "Python": "3.8.16", "Stable-Baselines3": "1.6.2", "PyTorch": "1.13.0+cu116", "GPU Enabled": "True", "Numpy": "1.21.6", "Gym": "0.21.0"}}
|
ppo-LunarLander-v2.zip
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:e7c764f9dece811bbc5ca9fd0b6283a30490838a80054d4a9ec88af447d59cea
|
| 3 |
+
size 147190
|
ppo-LunarLander-v2/_stable_baselines3_version
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
1.6.2
|
ppo-LunarLander-v2/data
ADDED
|
@@ -0,0 +1,94 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 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 sde_net_arch: Network architecture for extracting features\n when using gSDE. If None, the latent features from the policy will be used.\n Pass an empty list to use the states as features.\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 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 0x7f4b32a4c790>",
|
| 8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f4b32a4c820>",
|
| 9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f4b32a4c8b0>",
|
| 10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f4b32a4c940>",
|
| 11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f4b32a4c9d0>",
|
| 12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f4b32a4ca60>",
|
| 13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f4b32a4caf0>",
|
| 14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f4b32a4cb80>",
|
| 15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f4b32a4cc10>",
|
| 16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f4b32a4cca0>",
|
| 17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f4b32a4cd30>",
|
| 18 |
+
"__abstractmethods__": "frozenset()",
|
| 19 |
+
"_abc_impl": "<_abc_data object at 0x7f4b32a42ed0>"
|
| 20 |
+
},
|
| 21 |
+
"verbose": 1,
|
| 22 |
+
"policy_kwargs": {},
|
| 23 |
+
"observation_space": {
|
| 24 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
| 25 |
+
":serialized:": "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",
|
| 26 |
+
"dtype": "float32",
|
| 27 |
+
"_shape": [
|
| 28 |
+
8
|
| 29 |
+
],
|
| 30 |
+
"low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
|
| 31 |
+
"high": "[inf inf inf inf inf inf inf inf]",
|
| 32 |
+
"bounded_below": "[False False False False False False False False]",
|
| 33 |
+
"bounded_above": "[False False False False False False False False]",
|
| 34 |
+
"_np_random": null
|
| 35 |
+
},
|
| 36 |
+
"action_space": {
|
| 37 |
+
":type:": "<class 'gym.spaces.discrete.Discrete'>",
|
| 38 |
+
":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
|
| 39 |
+
"n": 4,
|
| 40 |
+
"_shape": [],
|
| 41 |
+
"dtype": "int64",
|
| 42 |
+
"_np_random": null
|
| 43 |
+
},
|
| 44 |
+
"n_envs": 16,
|
| 45 |
+
"num_timesteps": 1015808,
|
| 46 |
+
"_total_timesteps": 1000000,
|
| 47 |
+
"_num_timesteps_at_start": 0,
|
| 48 |
+
"seed": null,
|
| 49 |
+
"action_noise": null,
|
| 50 |
+
"start_time": 1670939936403238818,
|
| 51 |
+
"learning_rate": 0.0003,
|
| 52 |
+
"tensorboard_log": null,
|
| 53 |
+
"lr_schedule": {
|
| 54 |
+
":type:": "<class 'function'>",
|
| 55 |
+
":serialized:": "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"
|
| 56 |
+
},
|
| 57 |
+
"_last_obs": {
|
| 58 |
+
":type:": "<class 'numpy.ndarray'>",
|
| 59 |
+
":serialized:": "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"
|
| 60 |
+
},
|
| 61 |
+
"_last_episode_starts": {
|
| 62 |
+
":type:": "<class 'numpy.ndarray'>",
|
| 63 |
+
":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
|
| 64 |
+
},
|
| 65 |
+
"_last_original_obs": null,
|
| 66 |
+
"_episode_num": 0,
|
| 67 |
+
"use_sde": false,
|
| 68 |
+
"sde_sample_freq": -1,
|
| 69 |
+
"_current_progress_remaining": -0.015808000000000044,
|
| 70 |
+
"ep_info_buffer": {
|
| 71 |
+
":type:": "<class 'collections.deque'>",
|
| 72 |
+
":serialized:": "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"
|
| 73 |
+
},
|
| 74 |
+
"ep_success_buffer": {
|
| 75 |
+
":type:": "<class 'collections.deque'>",
|
| 76 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
| 77 |
+
},
|
| 78 |
+
"_n_updates": 248,
|
| 79 |
+
"n_steps": 1024,
|
| 80 |
+
"gamma": 0.999,
|
| 81 |
+
"gae_lambda": 0.98,
|
| 82 |
+
"ent_coef": 0.01,
|
| 83 |
+
"vf_coef": 0.5,
|
| 84 |
+
"max_grad_norm": 0.5,
|
| 85 |
+
"batch_size": 64,
|
| 86 |
+
"n_epochs": 4,
|
| 87 |
+
"clip_range": {
|
| 88 |
+
":type:": "<class 'function'>",
|
| 89 |
+
":serialized:": "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"
|
| 90 |
+
},
|
| 91 |
+
"clip_range_vf": null,
|
| 92 |
+
"normalize_advantage": true,
|
| 93 |
+
"target_kl": null
|
| 94 |
+
}
|
ppo-LunarLander-v2/policy.optimizer.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:8c078ff43c6acf1642c5cb199167f1d53b2bdb097025470b7345e0df0bfb7490
|
| 3 |
+
size 87929
|
ppo-LunarLander-v2/policy.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:a8efb5f3a3ab0b730a101db28e80c2e6af835574bb6404adddb12c1060076f9f
|
| 3 |
+
size 43201
|
ppo-LunarLander-v2/pytorch_variables.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
|
| 3 |
+
size 431
|
ppo-LunarLander-v2/system_info.txt
ADDED
|
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
OS: Linux-5.10.133+-x86_64-with-glibc2.27 #1 SMP Fri Aug 26 08:44:51 UTC 2022
|
| 2 |
+
Python: 3.8.16
|
| 3 |
+
Stable-Baselines3: 1.6.2
|
| 4 |
+
PyTorch: 1.13.0+cu116
|
| 5 |
+
GPU Enabled: True
|
| 6 |
+
Numpy: 1.21.6
|
| 7 |
+
Gym: 0.21.0
|
replay.mp4
ADDED
|
Binary file (195 kB). View file
|
|
|
results.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"mean_reward": 266.68098818265815, "std_reward": 18.95113985037078, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-13T14:59:41.217062"}
|