first PPO, default SB3 parameters
Browse files- README.md +37 -0
- config.json +1 -0
- kingkw1_ppo_lunarlander.zip +3 -0
- kingkw1_ppo_lunarlander/_stable_baselines3_version +1 -0
- kingkw1_ppo_lunarlander/data +94 -0
- kingkw1_ppo_lunarlander/policy.optimizer.pth +3 -0
- kingkw1_ppo_lunarlander/policy.pth +3 -0
- kingkw1_ppo_lunarlander/pytorch_variables.pth +3 -0
- kingkw1_ppo_lunarlander/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: 265.52 +/- 22.03
|
| 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 0x7f8fc9881550>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8fc98815e0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8fc9881670>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8fc9881700>", "_build": "<function ActorCriticPolicy._build at 0x7f8fc9881790>", "forward": "<function ActorCriticPolicy.forward at 0x7f8fc9881820>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f8fc98818b0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f8fc9881940>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f8fc98819d0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f8fc9881a60>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f8fc9881af0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f8fc9878ed0>"}, "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": 1672342106511156628, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4BDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/M6kqMFUyYYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_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": 310, "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, "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"}}
|
kingkw1_ppo_lunarlander.zip
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c86ea36068ed9baaf63afbd52646fc2c52a223d5c857f76057a4fd83ba23499a
|
| 3 |
+
size 147121
|
kingkw1_ppo_lunarlander/_stable_baselines3_version
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
1.6.2
|
kingkw1_ppo_lunarlander/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 0x7f8fc9881550>",
|
| 8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f8fc98815e0>",
|
| 9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f8fc9881670>",
|
| 10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f8fc9881700>",
|
| 11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f8fc9881790>",
|
| 12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f8fc9881820>",
|
| 13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f8fc98818b0>",
|
| 14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f8fc9881940>",
|
| 15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f8fc98819d0>",
|
| 16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f8fc9881a60>",
|
| 17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f8fc9881af0>",
|
| 18 |
+
"__abstractmethods__": "frozenset()",
|
| 19 |
+
"_abc_impl": "<_abc_data object at 0x7f8fc9878ed0>"
|
| 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": 1672342106511156628,
|
| 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": 310,
|
| 79 |
+
"n_steps": 2048,
|
| 80 |
+
"gamma": 0.99,
|
| 81 |
+
"gae_lambda": 0.95,
|
| 82 |
+
"ent_coef": 0.0,
|
| 83 |
+
"vf_coef": 0.5,
|
| 84 |
+
"max_grad_norm": 0.5,
|
| 85 |
+
"batch_size": 64,
|
| 86 |
+
"n_epochs": 10,
|
| 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 |
+
}
|
kingkw1_ppo_lunarlander/policy.optimizer.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:3c9a84b75d6cef49e565bf06c414492876012ba4274172ab203204d19d6f0982
|
| 3 |
+
size 87929
|
kingkw1_ppo_lunarlander/policy.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:17ddf7505d3a9b52a1432c8037ee84460e1199f4af3c6c83b9289af755128528
|
| 3 |
+
size 43201
|
kingkw1_ppo_lunarlander/pytorch_variables.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:d030ad8db708280fcae77d87e973102039acd23a11bdecc3db8eb6c0ac940ee1
|
| 3 |
+
size 431
|
kingkw1_ppo_lunarlander/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 (221 kB). View file
|
|
|
results.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"mean_reward": 265.51612583975583, "std_reward": 22.02841302047516, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-12-29T20:20:10.201950"}
|