Uploading PPO-LunarLander-v2 trained agent
Browse files- .gitattributes +1 -0
- README.md +28 -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 +3 -0
- results.json +1 -0
.gitattributes
CHANGED
@@ -25,3 +25,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
25 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
26 |
*.zstandard filter=lfs diff=lfs merge=lfs -text
|
27 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
|
|
|
25 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
26 |
*.zstandard filter=lfs diff=lfs merge=lfs -text
|
27 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
28 |
+
*.mp4 filter=lfs diff=lfs merge=lfs -text
|
README.md
ADDED
@@ -0,0 +1,28 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 |
+
- metrics:
|
12 |
+
- type: mean_reward
|
13 |
+
value: 218.15 +/- 9.07
|
14 |
+
name: mean_reward
|
15 |
+
task:
|
16 |
+
type: reinforcement-learning
|
17 |
+
name: reinforcement-learning
|
18 |
+
dataset:
|
19 |
+
name: LunarLander-v2
|
20 |
+
type: LunarLander-v2
|
21 |
+
---
|
22 |
+
|
23 |
+
# **PPO** Agent playing **LunarLander-v2**
|
24 |
+
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
|
25 |
+
|
26 |
+
## Usage (with Stable-baselines3)
|
27 |
+
TODO: Add your code
|
28 |
+
|
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 0x7f47f384f560>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f47f384f5f0>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f47f384f680>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f47f384f710>", "_build": "<function ActorCriticPolicy._build at 0x7f47f384f7a0>", "forward": "<function ActorCriticPolicy.forward at 0x7f47f384f830>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f47f384f8c0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f47f384f950>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f47f384f9e0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f47f384fa70>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f47f384fb00>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f47f3822480>"}, "verbose": 1, "policy_kwargs": {}, "observation_space": {":type:": "<class 'gym.spaces.box.Box'>", ":serialized:": "gAWVnwEAAAAAAACMDmd5bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lGgFk5SMAmY0lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMBl9zaGFwZZRLCIWUjANsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWIAAAAAAAAAAAAID/AACA/wAAgP8AAID/AACA/wAAgP8AAID/AACA/5RoCksIhZSMAUOUdJRSlIwEaGlnaJRoEiiWIAAAAAAAAAAAAIB/AACAfwAAgH8AAIB/AACAfwAAgH8AAIB/AACAf5RoCksIhZRoFXSUUpSMDWJvdW5kZWRfYmVsb3eUaBIolggAAAAAAAAAAAAAAAAAAACUaAeMAmIxlImIh5RSlChLA4wBfJROTk5K/////0r/////SwB0lGJLCIWUaBV0lFKUjA1ib3VuZGVkX2Fib3ZllGgSKJYIAAAAAAAAAAAAAAAAAAAAlGghSwiFlGgVdJRSlIwKX25wX3JhbmRvbZROdWIu", "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": 1651674869.1160164, "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": 155, "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": 5, "clip_range": {":type:": "<class 'function'>", ":serialized:": "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"}, "clip_range_vf": null, "normalize_advantage": true, "target_kl": null, "system_info": {"OS": "Linux-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022", "Python": "3.7.13", "Stable-Baselines3": "1.5.0", "PyTorch": "1.11.0+cu113", "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:f38a7dde31c5f70d49d89d7c5c73502fce87a30da94b163d0b69e950a8a02b0e
|
3 |
+
size 144048
|
ppo-LunarLander-v2/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
1.5.0
|
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 0x7f47f384f560>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f47f384f5f0>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f47f384f680>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f47f384f710>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f47f384f7a0>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f47f384f830>",
|
13 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f47f384f8c0>",
|
14 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f47f384f950>",
|
15 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f47f384f9e0>",
|
16 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f47f384fa70>",
|
17 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f47f384fb00>",
|
18 |
+
"__abstractmethods__": "frozenset()",
|
19 |
+
"_abc_impl": "<_abc_data object at 0x7f47f3822480>"
|
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": 1651674869.1160164,
|
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:": "gAWVdQIAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYAAgAAAAAAAFMrDT7s0be7zs0LPdxuQbt94Bu97hEkvAAAAAAAAIA/E6Qwvl7t+T4q62G9mQSYvk/Bkb0uB4C9AAAAAAAAAAAz+x+9hcviuaHTFznlC4M0dl4mOyZhL7gAAIA/AACAP2bOXzt7EI26UtqGu9KZATuDFCU7wU4PvAAAgD8AAIA/8wGaveFgpbra4eo6FQSKNdNt1bjSEAe6AACAPwAAgD9NArC9e4iNugz/NLmEXZK12bsMO9Y0WDgAAIA/AACAP5q9jDsfPcO54L9/unPQgbZ55B26WVKTOQAAgD8AAIA/Guc0PR8l+jgbndA7oe4mODsr57sjsei3AACAPwAAgD/O64O+uR+gP4Axub6dML++jmGYvhhOkrwAAAAAAAAAAGbP/D1bK4Y/wxxVPs0YlL4A/Tk9NjeAvAAAAAAAAAAAsxT0vdmEMT4EWL89kWH0vUIagLxTrd+9AAAAAAAAAADzbTC+lFmXvMtz5zqH0Dk52kQFPriFHroAAIA/AACAPzPhT7zskZe5syXnOiu+SbX6/dK7ppEJugAAgD8AAIA/zRkcvVJw/bnOlIK5TXgPtXOnPzsrSZk4AACAPwAAgD8NPuY9KZhduhJB7bseK3826OZhO3BR6LUAAAAAAACAPypJlz6LUKY/ZkTwPufStb4le5o+Uzu9OwAAAAAAAAAAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjSUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksQSwiGlIwBQ5R0lFKULg=="
|
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": 155,
|
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": 5,
|
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 sha256:4944e2c5714b7a69bbd2554d098af404fac055415fbfd40bf91363d82ab2ba07
|
3 |
+
size 84829
|
ppo-LunarLander-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ab5722baa438c4c48d159925b3c0d6f98a25617252e79c25fb5a2bbd66c3de8a
|
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-5.4.188+-x86_64-with-Ubuntu-18.04-bionic #1 SMP Sun Apr 24 10:03:06 PDT 2022
|
2 |
+
Python: 3.7.13
|
3 |
+
Stable-Baselines3: 1.5.0
|
4 |
+
PyTorch: 1.11.0+cu113
|
5 |
+
GPU Enabled: True
|
6 |
+
Numpy: 1.21.6
|
7 |
+
Gym: 0.21.0
|
replay.mp4
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:33b7dfd28eb5ccbc24c23e039d87c3e7b41b7cd80c272c970790e9726bef5abc
|
3 |
+
size 252578
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 218.15029398922397, "std_reward": 9.06994623005047, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2022-05-04T14:59:10.560386"}
|