Johannes Weyel
commited on
Commit
·
c6b702b
1
Parent(s):
21e603f
late submission :)
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 +95 -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: 249.44 +/- 24.52
|
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 0x7f01a29575e0>", "_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f01a2957670>", "reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f01a2957700>", "_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f01a2957790>", "_build": "<function ActorCriticPolicy._build at 0x7f01a2957820>", "forward": "<function ActorCriticPolicy.forward at 0x7f01a29578b0>", "extract_features": "<function ActorCriticPolicy.extract_features at 0x7f01a2957940>", "_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f01a29579d0>", "_predict": "<function ActorCriticPolicy._predict at 0x7f01a2957a60>", "evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f01a2957af0>", "get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f01a2957b80>", "predict_values": "<function ActorCriticPolicy.predict_values at 0x7f01a2957c10>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc_data object at 0x7f01a29d1a50>"}, "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": 1015808, "_total_timesteps": 1000000, "_num_timesteps_at_start": 0, "seed": null, "action_noise": null, "start_time": 1674063448952227426, "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": 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-5.15.0-58-generic-x86_64-with-glibc2.29 # 64~20.04.1-Ubuntu SMP Fri Jan 6 16:42:31 UTC 2023", "Python": "3.8.10", "Stable-Baselines3": "1.7.0", "PyTorch": "1.11.0+cu113", "GPU Enabled": "True", "Numpy": "1.24.1", "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:a7f38ef9dd5027c17941eb349519e4252cbc934aca94659a30c85c9776d3cab3
|
3 |
+
size 144787
|
ppo-LunarLander-v2/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
1.7.0
|
ppo-LunarLander-v2/data
ADDED
@@ -0,0 +1,95 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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 0x7f01a29575e0>",
|
8 |
+
"_get_constructor_parameters": "<function ActorCriticPolicy._get_constructor_parameters at 0x7f01a2957670>",
|
9 |
+
"reset_noise": "<function ActorCriticPolicy.reset_noise at 0x7f01a2957700>",
|
10 |
+
"_build_mlp_extractor": "<function ActorCriticPolicy._build_mlp_extractor at 0x7f01a2957790>",
|
11 |
+
"_build": "<function ActorCriticPolicy._build at 0x7f01a2957820>",
|
12 |
+
"forward": "<function ActorCriticPolicy.forward at 0x7f01a29578b0>",
|
13 |
+
"extract_features": "<function ActorCriticPolicy.extract_features at 0x7f01a2957940>",
|
14 |
+
"_get_action_dist_from_latent": "<function ActorCriticPolicy._get_action_dist_from_latent at 0x7f01a29579d0>",
|
15 |
+
"_predict": "<function ActorCriticPolicy._predict at 0x7f01a2957a60>",
|
16 |
+
"evaluate_actions": "<function ActorCriticPolicy.evaluate_actions at 0x7f01a2957af0>",
|
17 |
+
"get_distribution": "<function ActorCriticPolicy.get_distribution at 0x7f01a2957b80>",
|
18 |
+
"predict_values": "<function ActorCriticPolicy.predict_values at 0x7f01a2957c10>",
|
19 |
+
"__abstractmethods__": "frozenset()",
|
20 |
+
"_abc_impl": "<_abc_data object at 0x7f01a29d1a50>"
|
21 |
+
},
|
22 |
+
"verbose": 1,
|
23 |
+
"policy_kwargs": {},
|
24 |
+
"observation_space": {
|
25 |
+
":type:": "<class 'gym.spaces.box.Box'>",
|
26 |
+
":serialized:": "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",
|
27 |
+
"dtype": "float32",
|
28 |
+
"_shape": [
|
29 |
+
8
|
30 |
+
],
|
31 |
+
"low": "[-inf -inf -inf -inf -inf -inf -inf -inf]",
|
32 |
+
"high": "[inf inf inf inf inf inf inf inf]",
|
33 |
+
"bounded_below": "[False False False False False False False False]",
|
34 |
+
"bounded_above": "[False False False False False False False False]",
|
35 |
+
"_np_random": null
|
36 |
+
},
|
37 |
+
"action_space": {
|
38 |
+
":type:": "<class 'gym.spaces.discrete.Discrete'>",
|
39 |
+
":serialized:": "gAWVggAAAAAAAACME2d5bS5zcGFjZXMuZGlzY3JldGWUjAhEaXNjcmV0ZZSTlCmBlH2UKIwBbpRLBIwGX3NoYXBllCmMBWR0eXBllIwFbnVtcHmUaAeTlIwCaTiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYowKX25wX3JhbmRvbZROdWIu",
|
40 |
+
"n": 4,
|
41 |
+
"_shape": [],
|
42 |
+
"dtype": "int64",
|
43 |
+
"_np_random": null
|
44 |
+
},
|
45 |
+
"n_envs": 16,
|
46 |
+
"num_timesteps": 1015808,
|
47 |
+
"_total_timesteps": 1000000,
|
48 |
+
"_num_timesteps_at_start": 0,
|
49 |
+
"seed": null,
|
50 |
+
"action_noise": null,
|
51 |
+
"start_time": 1674063448952227426,
|
52 |
+
"learning_rate": 0.0003,
|
53 |
+
"tensorboard_log": null,
|
54 |
+
"lr_schedule": {
|
55 |
+
":type:": "<class 'function'>",
|
56 |
+
":serialized:": "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"
|
57 |
+
},
|
58 |
+
"_last_obs": {
|
59 |
+
":type:": "<class 'numpy.ndarray'>",
|
60 |
+
":serialized:": "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"
|
61 |
+
},
|
62 |
+
"_last_episode_starts": {
|
63 |
+
":type:": "<class 'numpy.ndarray'>",
|
64 |
+
":serialized:": "gAWVgwAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACUjAVudW1weZSMBWR0eXBllJOUjAJiMZSJiIeUUpQoSwOMAXyUTk5OSv////9K/////0sAdJRiSxCFlIwBQ5R0lFKULg=="
|
65 |
+
},
|
66 |
+
"_last_original_obs": null,
|
67 |
+
"_episode_num": 0,
|
68 |
+
"use_sde": false,
|
69 |
+
"sde_sample_freq": -1,
|
70 |
+
"_current_progress_remaining": -0.015808000000000044,
|
71 |
+
"ep_info_buffer": {
|
72 |
+
":type:": "<class 'collections.deque'>",
|
73 |
+
":serialized:": "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"
|
74 |
+
},
|
75 |
+
"ep_success_buffer": {
|
76 |
+
":type:": "<class 'collections.deque'>",
|
77 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
78 |
+
},
|
79 |
+
"_n_updates": 248,
|
80 |
+
"n_steps": 1024,
|
81 |
+
"gamma": 0.999,
|
82 |
+
"gae_lambda": 0.98,
|
83 |
+
"ent_coef": 0.01,
|
84 |
+
"vf_coef": 0.5,
|
85 |
+
"max_grad_norm": 0.5,
|
86 |
+
"batch_size": 64,
|
87 |
+
"n_epochs": 4,
|
88 |
+
"clip_range": {
|
89 |
+
":type:": "<class 'function'>",
|
90 |
+
":serialized:": "gAWVawMAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMnC9ob21lL3VzZXJsL0dJVHMvSHVnZ2luZ2ZhY2UvRGVlcF9STF9Db3Vyc2VfcGxheWdyb3VuZC9kZWVwLXJsLWNsYXNzL25vdGVib29rcy91bml0MS91bml0MS1lbnYvbGliL3B5dGhvbjMuOC9zaXRlLXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4JDAgABlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMnC9ob21lL3VzZXJsL0dJVHMvSHVnZ2luZ2ZhY2UvRGVlcF9STF9Db3Vyc2VfcGxheWdyb3VuZC9kZWVwLXJsLWNsYXNzL25vdGVib29rcy91bml0MS91bml0MS1lbnYvbGliL3B5dGhvbjMuOC9zaXRlLXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/yZmZmZmZmoWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="
|
91 |
+
},
|
92 |
+
"clip_range_vf": null,
|
93 |
+
"normalize_advantage": true,
|
94 |
+
"target_kl": null
|
95 |
+
}
|
ppo-LunarLander-v2/policy.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:ca1a715d7622788726d40be1296cc55d032155da30190228ce16ca77c130e916
|
3 |
+
size 84829
|
ppo-LunarLander-v2/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:345ca6530071b983f01fcf9ba26d8bb89f6ab02a9cb11c612f377828a12547b5
|
3 |
+
size 43393
|
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-5.15.0-58-generic-x86_64-with-glibc2.29 # 64~20.04.1-Ubuntu SMP Fri Jan 6 16:42:31 UTC 2023
|
2 |
+
- Python: 3.8.10
|
3 |
+
- Stable-Baselines3: 1.7.0
|
4 |
+
- PyTorch: 1.11.0+cu113
|
5 |
+
- GPU Enabled: True
|
6 |
+
- Numpy: 1.24.1
|
7 |
+
- Gym: 0.21.0
|
replay.mp4
ADDED
Binary file (241 kB). View file
|
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 249.43898254181917, "std_reward": 24.51849243464459, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-01-18T19:04:52.001526"}
|