First trained RL model
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: 171.84 +/- 51.84
|
| 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 0x7f606fe61700>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f606fe61790>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f606fe61820>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f606fe618b0>", "_build": "<function ActorCriticPolicy._build at 0x7f606fe61940>", "forward": "<function ActorCriticPolicy.forward at 0x7f606fe619d0>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f606fe61a60>", "_predict": "<function ActorCriticPolicy._predict at 0x7f606fe61af0>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f606fe61b80>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f606fe61c10>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f606fe61ca0>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f606fe59a50>"}, "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": 507904, "_total_timesteps": 500000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1672751389584710932, "learning_rate": 0.0003, "tensorboard_log": null, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "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"}, "_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": 124, "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 sha256:7f653d57df3e2e2039001cf3fa6717b8be79a4c8259e3d3b3d27ffe56f15bb7a
|
| 3 |
+
size 147212
|
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 0x7f606fe61700>",
|
| 8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f606fe61790>",
|
| 9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f606fe61820>",
|
| 10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f606fe618b0>",
|
| 11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f606fe61940>",
|
| 12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f606fe619d0>",
|
| 13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f606fe61a60>",
|
| 14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f606fe61af0>",
|
| 15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f606fe61b80>",
|
| 16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f606fe61c10>",
|
| 17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f606fe61ca0>",
|
| 18 |
+
"__abstractmethods__": "frozenset()",
|
| 19 |
+
"_abc_impl": "<_abc_data object at 0x7f606fe59a50>"
|
| 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": 507904,
|
| 46 |
+
"_total_timesteps": 500000,
|
| 47 |
+
"_num_timesteps_at_start": 0,
|
| 48 |
+
"seed": null,
|
| 49 |
+
"action_noise": null,
|
| 50 |
+
"start_time": 1672751389584710932,
|
| 51 |
+
"learning_rate": 0.0003,
|
| 52 |
+
"tensorboard_log": null,
|
| 53 |
+
"lr_schedule": {
|
| 54 |
+
":type:": "<class 'function'>",
|
| 55 |
+
":serialized:": "gAWVwwIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4BDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMSC91c3IvbG9jYWwvbGliL3B5dGhvbjMuOC9kaXN0LXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/M6kqMFUyYYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
|
| 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": 124,
|
| 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 sha256:146474d050866df58c341a3405dfa70ba66f74c3527687e3faec4946e00dcb66
|
| 3 |
+
size 87929
|
ppo-LunarLander-v2/policy.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:609c6cd638a795c5327d323eb3a53e10ae577b78d8198b6dbb9eb1981795bc95
|
| 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 (262 kB). View file
|
|
|
results.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{"mean_reward": 171.84061307266435, "std_reward": 51.835384837277694, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-03T13:36:08.924425"}
|