Commit
·
80a4ea3
1
Parent(s):
e9757c1
model
Browse files- .gitattributes +1 -0
- README.md +35 -1
- config.json +1 -0
- replay.mp4 +3 -0
- results.json +1 -0
- walker2d-v5-sac-expert.zip +3 -0
- walker2d-v5-sac-expert/_stable_baselines3_version +1 -0
- walker2d-v5-sac-expert/actor.optimizer.pth +3 -0
- walker2d-v5-sac-expert/critic.optimizer.pth +3 -0
- walker2d-v5-sac-expert/data +124 -0
- walker2d-v5-sac-expert/ent_coef_optimizer.pth +3 -0
- walker2d-v5-sac-expert/policy.pth +3 -0
- walker2d-v5-sac-expert/pytorch_variables.pth +3 -0
- walker2d-v5-sac-expert/system_info.txt +9 -0
.gitattributes
CHANGED
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
33 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
34 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
|
|
|
33 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
34 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
36 |
+
replay.mp4 filter=lfs diff=lfs merge=lfs -text
|
README.md
CHANGED
@@ -1,3 +1,37 @@
|
|
1 |
---
|
2 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
3 |
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
---
|
2 |
+
library_name: stable-baselines3
|
3 |
+
tags:
|
4 |
+
- Walker2d-v5
|
5 |
+
- deep-reinforcement-learning
|
6 |
+
- reinforcement-learning
|
7 |
+
- stable-baselines3
|
8 |
+
model-index:
|
9 |
+
- name: SAC
|
10 |
+
results:
|
11 |
+
- task:
|
12 |
+
type: reinforcement-learning
|
13 |
+
name: reinforcement-learning
|
14 |
+
dataset:
|
15 |
+
name: Walker2d-v5
|
16 |
+
type: Walker2d-v5
|
17 |
+
metrics:
|
18 |
+
- type: mean_reward
|
19 |
+
value: 5044.42 +/- 45.44
|
20 |
+
name: mean_reward
|
21 |
+
verified: false
|
22 |
---
|
23 |
+
|
24 |
+
# **SAC** Agent playing **Walker2d-v5**
|
25 |
+
This is a trained model of a **SAC** agent playing **Walker2d-v5**
|
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:": "gAWVMAAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLnNhYy5wb2xpY2llc5SMCVNBQ1BvbGljeZSTlC4=", "__module__": "stable_baselines3.sac.policies", "__annotations__": "{'actor': <class 'stable_baselines3.sac.policies.Actor'>, 'critic': <class 'stable_baselines3.common.policies.ContinuousCritic'>, 'critic_target': <class 'stable_baselines3.common.policies.ContinuousCritic'>}", "__doc__": "\n Policy class (with both actor and critic) for SAC.\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 use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE 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 clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\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 :param n_critics: Number of critic networks to create.\n :param share_features_extractor: Whether to share or not the features extractor\n between the actor and the critic (this saves computation time)\n ", "__init__": "<function SACPolicy.__init__ at 0x7fe810877e20>", "_build": "<function SACPolicy._build at 0x7fe810877eb0>", "_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 0x7fe810877f40>", "reset_noise": "<function SACPolicy.reset_noise at 0x7fe81089c040>", "make_actor": "<function SACPolicy.make_actor at 0x7fe81089c0d0>", "make_critic": "<function SACPolicy.make_critic at 0x7fe81089c160>", "forward": "<function SACPolicy.forward at 0x7fe81089c1f0>", "_predict": "<function SACPolicy._predict at 0x7fe81089c280>", "set_training_mode": "<function SACPolicy.set_training_mode at 0x7fe81089c310>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fe810891b00>"}, "verbose": 0, "policy_kwargs": {"use_sde": false}, "num_timesteps": 3000000, "_total_timesteps": 3000000, "_num_timesteps_at_start": 0, "seed": 2, "action_noise": null, "start_time": 1699326763247033694, "learning_rate": 0.0003, "tensorboard_log": "runs/hwmxww94", "_last_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_last_episode_starts": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "gAWVeAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYFAAAAAAAAAAEBAQEBlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksFhZSMAUOUdJRSlC4="}, "_last_original_obs": {":type:": "<class 'numpy.ndarray'>", ":serialized:": "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"}, "_episode_num": 4864, "use_sde": false, "sde_sample_freq": -1, "_current_progress_remaining": 0.0, "_stats_window_size": 100, "ep_info_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "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"}, "ep_success_buffer": {":type:": "<class 'collections.deque'>", ":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="}, "_n_updates": 598000, "buffer_size": 1000000, "batch_size": 256, "learning_starts": 10000, "tau": 0.005, "gamma": 0.99, "gradient_steps": 1, "optimize_memory_usage": false, "replay_buffer_class": {":type:": "<class 'abc.ABCMeta'>", ":serialized:": "gAWVNQAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwMUmVwbGF5QnVmZmVylJOULg==", "__module__": "stable_baselines3.common.buffers", "__doc__": "\n Replay buffer used in off-policy algorithms like SAC/TD3.\n\n :param buffer_size: Max number of element in the buffer\n :param observation_space: Observation space\n :param action_space: Action space\n :param device: PyTorch device\n :param n_envs: Number of parallel environments\n :param optimize_memory_usage: Enable a memory efficient variant\n of the replay buffer which reduces by almost a factor two the memory used,\n at a cost of more complexity.\n See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n and https://github.com/DLR-RM/stable-baselines3/pull/28#issuecomment-637559274\n Cannot be used in combination with handle_timeout_termination.\n :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n separately and treat the task as infinite horizon task.\n https://github.com/DLR-RM/stable-baselines3/issues/284\n ", "__init__": "<function ReplayBuffer.__init__ at 0x7fe81086ab90>", "add": "<function ReplayBuffer.add at 0x7fe81086ac20>", "sample": "<function ReplayBuffer.sample at 0x7fe81086acb0>", "_get_samples": "<function ReplayBuffer._get_samples at 0x7fe81086ad40>", "_maybe_cast_dtype": "<staticmethod(<function ReplayBuffer._maybe_cast_dtype at 0x7fe81086add0>)>", "__abstractmethods__": "frozenset()", "_abc_impl": "<_abc._abc_data object at 0x7fe8109d1600>"}, "replay_buffer_kwargs": {}, "train_freq": {":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>", ":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLAWgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu"}, "use_sde_at_warmup": false, "target_entropy": -6.0, "ent_coef": "auto", "target_update_interval": 1, "observation_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "gAWVsQIAAAAAAACMFGd5bW5hc2l1bS5zcGFjZXMuYm94lIwDQm94lJOUKYGUfZQojAVkdHlwZZSMBW51bXB5lIwFZHR5cGWUk5SMAmY4lImIh5RSlChLA4wBPJROTk5K/////0r/////SwB0lGKMDWJvdW5kZWRfYmVsb3eUjBJudW1weS5jb3JlLm51bWVyaWOUjAtfZnJvbWJ1ZmZlcpSTlCiWEQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJRoCIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksRhZSMAUOUdJRSlIwNYm91bmRlZF9hYm92ZZRoESiWEQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJRoFUsRhZRoGXSUUpSMBl9zaGFwZZRLEYWUjANsb3eUaBEologAAAAAAAAAAAAAAAAA8P8AAAAAAADw/wAAAAAAAPD/AAAAAAAA8P8AAAAAAADw/wAAAAAAAPD/AAAAAAAA8P8AAAAAAADw/wAAAAAAAPD/AAAAAAAA8P8AAAAAAADw/wAAAAAAAPD/AAAAAAAA8P8AAAAAAADw/wAAAAAAAPD/AAAAAAAA8P8AAAAAAADw/5RoC0sRhZRoGXSUUpSMBGhpZ2iUaBEologAAAAAAAAAAAAAAAAA8H8AAAAAAADwfwAAAAAAAPB/AAAAAAAA8H8AAAAAAADwfwAAAAAAAPB/AAAAAAAA8H8AAAAAAADwfwAAAAAAAPB/AAAAAAAA8H8AAAAAAADwfwAAAAAAAPB/AAAAAAAA8H8AAAAAAADwfwAAAAAAAPB/AAAAAAAA8H8AAAAAAADwf5RoC0sRhZRoGXSUUpSMCGxvd19yZXBylIwELWluZpSMCWhpZ2hfcmVwcpSMA2luZpSMCl9ucF9yYW5kb22UTnViLg==", "dtype": "float64", "bounded_below": "[False False False False False False False False False False False False\n False False False False False]", "bounded_above": "[False False False False False False False False False False False False\n False False False False False]", "_shape": [17], "low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf]", "high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf]", "low_repr": "-inf", "high_repr": "inf", "_np_random": null}, "action_space": {":type:": "<class 'gymnasium.spaces.box.Box'>", ":serialized:": "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", "dtype": "float32", "bounded_below": "[ True True True True True True]", "bounded_above": "[ True True True True True True]", "_shape": [6], "low": "[-1. -1. -1. -1. -1. -1.]", "high": "[1. 1. 1. 1. 1. 1.]", "low_repr": "-1.0", "high_repr": "1.0", "_np_random": "Generator(PCG64)"}, "n_envs": 5, "lr_schedule": {":type:": "<class 'function'>", ":serialized:": "gAWV8wIAAAAAAACMF2Nsb3VkcGlja2xlLmNsb3VkcGlja2xllIwOX21ha2VfZnVuY3Rpb26Uk5QoaACMDV9idWlsdGluX3R5cGWUk5SMCENvZGVUeXBllIWUUpQoSwFLAEsASwFLAUsTQwSIAFMAlE6FlCmMAV+UhZSMYC9ob21lL3JvZHJpL2FuYWNvbmRhMy9lbnZzL21pbmFyaS9saWIvcHl0aG9uMy4xMC9zaXRlLXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZSMBGZ1bmOUS4NDAgQBlIwDdmFslIWUKXSUUpR9lCiMC19fcGFja2FnZV9flIwYc3RhYmxlX2Jhc2VsaW5lczMuY29tbW9ulIwIX19uYW1lX1+UjB5zdGFibGVfYmFzZWxpbmVzMy5jb21tb24udXRpbHOUjAhfX2ZpbGVfX5SMYC9ob21lL3JvZHJpL2FuYWNvbmRhMy9lbnZzL21pbmFyaS9saWIvcHl0aG9uMy4xMC9zaXRlLXBhY2thZ2VzL3N0YWJsZV9iYXNlbGluZXMzL2NvbW1vbi91dGlscy5weZR1Tk5oAIwQX21ha2VfZW1wdHlfY2VsbJSTlClSlIWUdJRSlIwcY2xvdWRwaWNrbGUuY2xvdWRwaWNrbGVfZmFzdJSMEl9mdW5jdGlvbl9zZXRzdGF0ZZSTlGgffZR9lChoFmgNjAxfX3F1YWxuYW1lX1+UjBljb25zdGFudF9mbi48bG9jYWxzPi5mdW5jlIwPX19hbm5vdGF0aW9uc19flH2UjA5fX2t3ZGVmYXVsdHNfX5ROjAxfX2RlZmF1bHRzX1+UTowKX19tb2R1bGVfX5RoF4wHX19kb2NfX5ROjAtfX2Nsb3N1cmVfX5RoAIwKX21ha2VfY2VsbJSTlEc/M6kqMFUyYYWUUpSFlIwXX2Nsb3VkcGlja2xlX3N1Ym1vZHVsZXOUXZSMC19fZ2xvYmFsc19flH2UdYaUhlIwLg=="}, "batch_norm_stats": [], "batch_norm_stats_target": [], "system_info": {"OS": "Linux-6.2.0-35-generic-x86_64-with-glibc2.35 # 35~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Fri Oct 6 10:23:26 UTC 2", "Python": "3.10.12", "Stable-Baselines3": "2.1.0", "PyTorch": "2.0.1+cu117", "GPU Enabled": "True", "Numpy": "1.26.1", "Cloudpickle": "2.2.1", "Gymnasium": "0.29.0", "OpenAI Gym": "0.23.1"}}
|
replay.mp4
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:8dcfeaddae38d4fc241b336a092bc367392c37a1b9f791562956c14876a3e23c
|
3 |
+
size 1197483
|
results.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"mean_reward": 5044.4153396, "std_reward": 45.4394362382702, "is_deterministic": true, "n_eval_episodes": 10, "eval_datetime": "2023-11-07T11:03:23.888623"}
|
walker2d-v5-sac-expert.zip
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:0bc5c479112b6046ed2656d6423b6333d22461c8d2dd53c1464dc8aac66c9b3e
|
3 |
+
size 3239724
|
walker2d-v5-sac-expert/_stable_baselines3_version
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
2.1.0
|
walker2d-v5-sac-expert/actor.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:250620d8e26640c5c2c07aa3ea67d8315e169bcb4ae9fc67359fea9225232cbf
|
3 |
+
size 594333
|
walker2d-v5-sac-expert/critic.optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:9a41285fac79ec9930d7a86bd83149f067984c3b76cc1cd9f433dbdcdab482e6
|
3 |
+
size 1164793
|
walker2d-v5-sac-expert/data
ADDED
@@ -0,0 +1,124 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"policy_class": {
|
3 |
+
":type:": "<class 'abc.ABCMeta'>",
|
4 |
+
":serialized:": "gAWVMAAAAAAAAACMHnN0YWJsZV9iYXNlbGluZXMzLnNhYy5wb2xpY2llc5SMCVNBQ1BvbGljeZSTlC4=",
|
5 |
+
"__module__": "stable_baselines3.sac.policies",
|
6 |
+
"__annotations__": "{'actor': <class 'stable_baselines3.sac.policies.Actor'>, 'critic': <class 'stable_baselines3.common.policies.ContinuousCritic'>, 'critic_target': <class 'stable_baselines3.common.policies.ContinuousCritic'>}",
|
7 |
+
"__doc__": "\n Policy class (with both actor and critic) for SAC.\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 use_sde: Whether to use State Dependent Exploration or not\n :param log_std_init: Initial value for the log standard deviation\n :param use_expln: Use ``expln()`` function instead of ``exp()`` when using gSDE 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 clip_mean: Clip the mean output when using gSDE to avoid numerical instability.\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 :param n_critics: Number of critic networks to create.\n :param share_features_extractor: Whether to share or not the features extractor\n between the actor and the critic (this saves computation time)\n ",
|
8 |
+
"__init__": "<function SACPolicy.__init__ at 0x7fe810877e20>",
|
9 |
+
"_build": "<function SACPolicy._build at 0x7fe810877eb0>",
|
10 |
+
"_get_constructor_parameters": "<function SACPolicy._get_constructor_parameters at 0x7fe810877f40>",
|
11 |
+
"reset_noise": "<function SACPolicy.reset_noise at 0x7fe81089c040>",
|
12 |
+
"make_actor": "<function SACPolicy.make_actor at 0x7fe81089c0d0>",
|
13 |
+
"make_critic": "<function SACPolicy.make_critic at 0x7fe81089c160>",
|
14 |
+
"forward": "<function SACPolicy.forward at 0x7fe81089c1f0>",
|
15 |
+
"_predict": "<function SACPolicy._predict at 0x7fe81089c280>",
|
16 |
+
"set_training_mode": "<function SACPolicy.set_training_mode at 0x7fe81089c310>",
|
17 |
+
"__abstractmethods__": "frozenset()",
|
18 |
+
"_abc_impl": "<_abc._abc_data object at 0x7fe810891b00>"
|
19 |
+
},
|
20 |
+
"verbose": 0,
|
21 |
+
"policy_kwargs": {
|
22 |
+
"use_sde": false
|
23 |
+
},
|
24 |
+
"num_timesteps": 3000000,
|
25 |
+
"_total_timesteps": 3000000,
|
26 |
+
"_num_timesteps_at_start": 0,
|
27 |
+
"seed": 2,
|
28 |
+
"action_noise": null,
|
29 |
+
"start_time": 1699326763247033694,
|
30 |
+
"learning_rate": 0.0003,
|
31 |
+
"tensorboard_log": "runs/hwmxww94",
|
32 |
+
"_last_obs": {
|
33 |
+
":type:": "<class 'numpy.ndarray'>",
|
34 |
+
":serialized:": "gAWVHQMAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJaoAgAAAAAAAP9X1MdwJPM/0I9mp+kr0j+OSHKtd0XAv7BrEhBpnKA/THIC+4Zs7z/81JZOeMd7v855tCMuuJk/ykNjuOaUgL/EgMTsioURQEnOYz5JkdU/YEAYZU84tD9CggJbCDASQHQMehaLkNy/trap0VS097+ZF8BATWYQwMYwvqX0kIi/v1i7CbNL+b9Jvk/YnQ/1P+pvGE6dtMk/JPWwTweNoD+qhJm010WNPzd1QqeZo+k/lA6/A+9q07/L2p/3j6SDPxL2dEWIQs8/lKu5lQ6XEkAsmFRtni/Zv+TYkBVkp/0/phtWsCwvwT+GqIK40KKAv46Bour8mMS/rFdnxb1MEUBiRSfdkEmavyovNZpnNB1AjUq/uDgR9T+z/4faiVK9P7sG2JN8jqY/KPwlXxIwlT9CeZk50prpP4nyo2T4Yd6/eKAN7W1qiT9gIE+Z587iv3j6PyaUmRFAkzIHzRD01j/wwy8muJXlvznOw78odok/wLDQFi4eoj8z1Rrh/dTGv8z/oI65pfG/hWc+sHFy0T+Sp//qf4UgQAoVvj/DsvQ/CPwsiD3WxD+QdPFtJYGiP0qUeydtdpE/88KIKFS66T9H4/3IQqrdv7NFATnCooc/eql6o3xc3b8hLsQB5+sRQD+rPfc6Ddg/Fg61WD57kz/yvA1KHe/FP17ZufBPTHC/S1RPcmKYuL/xu9+bi8Oyv4SnYsv60qI/1LVBimCYFUBX1WTeylz1PyIpPWxHao4//vpcDobboj9oyszFuUeVPyTbxxH8Feo/q7HI+wdc4r8wHi/u2FJkPz4jyK33ety/qmNdHkQgE0ADpu/BN3HhPzMW1OGgAN0/sbZfNV7LtL8fb4exAB2eP2DYMTtKRbA/1pq5Mu0W1z8ARQB/OX+kPynTvIjjPRhAlIwFbnVtcHmUjAVkdHlwZZSTlIwCZjiUiYiHlFKUKEsDjAE8lE5OTkr/////Sv////9LAHSUYksFSxGGlIwBQ5R0lFKULg=="
|
35 |
+
},
|
36 |
+
"_last_episode_starts": {
|
37 |
+
":type:": "<class 'numpy.ndarray'>",
|
38 |
+
":serialized:": "gAWVeAAAAAAAAACMEm51bXB5LmNvcmUubnVtZXJpY5SMC19mcm9tYnVmZmVylJOUKJYFAAAAAAAAAAEBAQEBlIwFbnVtcHmUjAVkdHlwZZSTlIwCYjGUiYiHlFKUKEsDjAF8lE5OTkr/////Sv////9LAHSUYksFhZSMAUOUdJRSlC4="
|
39 |
+
},
|
40 |
+
"_last_original_obs": {
|
41 |
+
":type:": "<class 'numpy.ndarray'>",
|
42 |
+
":serialized:": "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"
|
43 |
+
},
|
44 |
+
"_episode_num": 4864,
|
45 |
+
"use_sde": false,
|
46 |
+
"sde_sample_freq": -1,
|
47 |
+
"_current_progress_remaining": 0.0,
|
48 |
+
"_stats_window_size": 100,
|
49 |
+
"ep_info_buffer": {
|
50 |
+
":type:": "<class 'collections.deque'>",
|
51 |
+
":serialized:": "gAWVPgwAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKUKH2UKIwBcpRHQLK6rYEnssyMAWyUTegDjAF0lEdAvg4QKu0TlHV9lChoBkdAsfXeWSlnAmgHTegDaAhHQL4Omq6vq1R1fZQoaAZHQLLd1BbfP5ZoB03oA2gIR0C+EZljRUm2dX2UKGgGR0CzDJJ79hqkaAdN6ANoCEdAvheHMRpUP3V9lChoBkdAsxI3+dbxE2gHTegDaAhHQL4ZJfhuO0d1fZQoaAZHQLKpe0e2d/doB03oA2gIR0C+GrKKxcFAdX2UKGgGR0CzCijSsr/baAdN6ANoCEdAvhs5HXmNi3V9lChoBkdAsyLIH9m6G2gHTegDaAhHQL4eLlgtvn91fZQoaAZHQLK5XbayrxRoB03oA2gIR0C+I+ZmEoOQdX2UKGgGR0CyvZH7YTTOaAdN6ANoCEdAviWdCCz1LHV9lChoBkdAsuA9t3wCsGgHTegDaAhHQL4nM1PnB+F1fZQoaAZHQLLN3Gecx0xoB03oA2gIR0C+J7hCtzS1dX2UKGgGR0Cy5idUfgaWaAdN6ANoCEdAviq1SrHU+nV9lChoBkdAsrmVszl90GgHTegDaAhHQL4wX38n/kx1fZQoaAZHQLKjWXdTHbRoB03oA2gIR0C+MgOCsfaIdX2UKGgGR0Cy0AbSApazaAdN6ANoCEdAvjObb/Ot4nV9lChoBkdAse/8kWykbmgHTegDaAhHQL40I/82rGR1fZQoaAZHQLL+Y1KGtZFoB03oA2gIR0C+NyQ80UGndX2UKGgGR0CyrYznJT2naAdN6ANoCEdAvjzN9+gDinV9lChoBke/+TaRISUTtmgHSwhoCEdAvjzmScLBsXV9lChoBkdAstYqalUIcGgHTegDaAhHQL4+boCuEEl1fZQoaAZHQLMEImbLEDRoB03oA2gIR0C+QBglByCGdX2UKGgGR0CzAyLFn7HiaAdN6ANoCEdAvkCelN1yNnV9lChoBkdAiejC53C9AWgHS/toCEdAvkGa5DqnnHV9lChoBkdAcrRMNc4YJmgHS35oCEdAvkGk/+sHSnV9lChoBke/+vLHMlkYoGgHSwhoCEdAvkGz1oQFtHV9lChoBkdAsp25V0cOsmgHTegDaAhHQL5DmnA6+391fZQoaAZHQIFhSfe1rqNoB0u2aAhHQL5D7R64Uex1fZQoaAZHQLLanTq0MPVoB03oA2gIR0C+SWhA4XGfdX2UKGgGR0CynL9AkcCHaAdN6ANoCEdAvk0R02cawXV9lChoBkdAsup7PszEaWgHTegDaAhHQL5OKVwgkkd1fZQoaAZHQLMLZ11nuiNoB03oA2gIR0C+UMUVrRBvdX2UKGgGR0CxyGVCb+cZaAdN6ANoCEdAvlEybF0gbXV9lChoBkdANSimMwUQCmgHS1FoCEdAvlJnqUu+RHV9lChoBkdAspgrluFYdWgHTegDaAhHQL5W3l1bJOp1fZQoaAZHQLKQxk2gnMNoB03oA2gIR0C+WoRA4XGfdX2UKGgGR0CytDj238XOaAdN6ANoCEdAvluZNbkfcXV9lChoBkdAsmyFSvTw2GgHTegDaAhHQL5dedGy5Zt1fZQoaAZHQLHKveF+NLloB03oA2gIR0C+Xtc2R7qqdX2UKGgGR0CxUPTKgZjyaAdN6ANoCEdAvmM6OEM9bHV9lChoBkdAsxLMneBQN2gHTegDaAhHQL5m02tuDSR1fZQoaAZHQLL4WdIXj2loB03oA2gIR0C+Z+fo3aSLdX2UKGgGR0Cy7oe7L+xXaAdN6ANoCEdAvmnE6QvHtHV9lChoBkdAstW0lu3tr2gHTegDaAhHQL5rF1TBInV1fZQoaAZHQLL2ZQK8cuJoB03oA2gIR0C+b4r5IpYtdX2UKGgGR0CyqI8vM8oyaAdN6ANoCEdAvnM2rvLHMnV9lChoBkdAstkzduYQa2gHTegDaAhHQL50VlXA/LV1fZQoaAZHQLNVd0NjLB9oB03oA2gIR0C+djVWsA/+dX2UKGgGR0CzMOCE6DGtaAdN6ANoCEdAvneLBl+VknV9lChoBkdAsj5LiKiwjmgHTegDaAhHQL579G+bmU51fZQoaAZHQLJEYm+j/MpoB03oA2gIR0C+f5gSBbwCdX2UKGgGR0Cy1OV1Oj7AaAdN6ANoCEdAvoC1wEQoTnV9lChoBkdAsthoo+fRNWgHTegDaAhHQL6Cr336AOJ1fZQoaAZHQLLyUUGVzIVoB03oA2gIR0C+hAEDp1RtdX2UKGgGR0CygJ+EVWS2aAdN6ANoCEdAvoiDxqfvnnV9lChoBkdAsu8obR4QjGgHTegDaAhHQL6MUOeJ53V1fZQoaAZHQLNTDJQcghdoB03oA2gIR0C+jYsg2ZRbdX2UKGgGR0CxojV81Gb1aAdN6ANoCEdAvo9ziJfplnV9lChoBkdAsxcjAAQxvmgHTegDaAhHQL6QyW5paid1fZQoaAZHQLLxI2ZAprloB03oA2gIR0C+lU60IC2ddX2UKGgGR0CzNEaSxJNCaAdN6ANoCEdAvpj5lAeJYXV9lChoBkdAsq7iNo8IRmgHTegDaAhHQL6aFSsKb8Z1fZQoaAZHQLMogabnX/ZoB03oA2gIR0C+m/UW2w3YdX2UKGgGR0Cjvusju8braAdNQAJoCEdAvpx8y1uzhXV9lChoBkdAszY1jBl+VmgHTegDaAhHQL6dR7GNrCZ1fZQoaAZHQLIHHzFMqSZoB03oA2gIR0C+pVTKgZjydX2UKGgGR0Cy2XlbzK9xaAdN6ANoCEdAvqZ53aBZp3V9lChoBkdAstd/JuEVWWgHTegDaAhHQL6oUcLSeAd1fZQoaAZHQLNJKL4vexhoB03oA2gIR0C+qNgc94eLdX2UKGgGR0CzIPlD4QBgaAdN6ANoCEdAvqmizgMtsnV9lChoBkdAsta6iSJTEWgHTegDaAhHQL6xpMgEEDB1fZQoaAZHQLMFDCgbp/xoB03oA2gIR0C+srmxIJ7cdX2UKGgGR0CzBCIcFQl9aAdN6ANoCEdAvrSY+lj3EnV9lChoBkdAswnvaHsTnWgHTegDaAhHQL61IjgAIY51fZQoaAZHQLLNAzeoDPpoB03oA2gIR0C+tfAuuievdX2UKGgGR0CzbcK6e5FxaAdN6ANoCEdAvr4KNZNfxHV9lChoBkdAszItA2Q4j2gHTegDaAhHQL6/IoB7u2J1fZQoaAZHQLNwEt2s7uFoB03oA2gIR0C+wPr2xptadX2UKGgGR0Cy9d/KuB+XaAdN6ANoCEdAvsGI8s+V1XV9lChoBkdAsyJy5xzaK2gHTegDaAhHQL7CWFrEcbR1fZQoaAZHQLLNk82aUiZoB03oA2gIR0C+yqzguRLcdX2UKGgGR0CyedCDM/yHaAdN6ANoCEdAvsvCf8MuvnV9lChoBkdAswL9yZKFqWgHTegDaAhHQL7NnbNbC791fZQoaAZHQLNdKneSB9VoB03oA2gIR0C+zidFBppOdX2UKGgGR0CzTl4oqkM1aAdN6ANoCEdAvs8F+BpYcXV9lChoBkdAs5S+x1PnCGgHTegDaAhHQL7XJCdBjWl1fZQoaAZHQLMFI3r2QGRoB03oA2gIR0C+2GIGpuMudX2UKGgGR0CzSD4Sg5BDaAdN6ANoCEdAvtpI7MgU13V9lChoBkdAsrqqaw2VFGgHTegDaAhHQL7a0vJA+px1fZQoaAZHQLNp6+aBqbloB03oA2gIR0C+26IyO7xvdX2UKGgGR0Cyopz50r9VaAdN6ANoCEdAvuPtmNBF/nV9lChoBkdAsxoakEcKgWgHTegDaAhHQL7lDw8W9Dh1fZQoaAZHQLNWSM7EHdJoB03oA2gIR0C+5uoGdI5HdX2UKGgGR0CznIDKs+3ZaAdN6ANoCEdAvudvO+qR2nV9lChoBkdAsxEXHktEomgHTegDaAhHQL7oQ8pkPMB1fZQoaAZHQLNHp3vQWvdoB03oA2gIR0C+8Frcwg1WdX2UKGgGR0CzXXgCnxaxaAdN6ANoCEdAvvF6NbTts3V9lChoBkdAszjK8/UvwmgHTegDaAhHQL7zXQXQ+ll1fZQoaAZHQLMOej/dZaFoB03oA2gIR0C+8+M2R7qqdX2UKGgGR0CzPBfhQ3xXaAdN6ANoCEdAvvSz9VFQVXVlLg=="
|
52 |
+
},
|
53 |
+
"ep_success_buffer": {
|
54 |
+
":type:": "<class 'collections.deque'>",
|
55 |
+
":serialized:": "gAWVIAAAAAAAAACMC2NvbGxlY3Rpb25zlIwFZGVxdWWUk5QpS2SGlFKULg=="
|
56 |
+
},
|
57 |
+
"_n_updates": 598000,
|
58 |
+
"buffer_size": 1000000,
|
59 |
+
"batch_size": 256,
|
60 |
+
"learning_starts": 10000,
|
61 |
+
"tau": 0.005,
|
62 |
+
"gamma": 0.99,
|
63 |
+
"gradient_steps": 1,
|
64 |
+
"optimize_memory_usage": false,
|
65 |
+
"replay_buffer_class": {
|
66 |
+
":type:": "<class 'abc.ABCMeta'>",
|
67 |
+
":serialized:": "gAWVNQAAAAAAAACMIHN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi5idWZmZXJzlIwMUmVwbGF5QnVmZmVylJOULg==",
|
68 |
+
"__module__": "stable_baselines3.common.buffers",
|
69 |
+
"__doc__": "\n Replay buffer used in off-policy algorithms like SAC/TD3.\n\n :param buffer_size: Max number of element in the buffer\n :param observation_space: Observation space\n :param action_space: Action space\n :param device: PyTorch device\n :param n_envs: Number of parallel environments\n :param optimize_memory_usage: Enable a memory efficient variant\n of the replay buffer which reduces by almost a factor two the memory used,\n at a cost of more complexity.\n See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195\n and https://github.com/DLR-RM/stable-baselines3/pull/28#issuecomment-637559274\n Cannot be used in combination with handle_timeout_termination.\n :param handle_timeout_termination: Handle timeout termination (due to timelimit)\n separately and treat the task as infinite horizon task.\n https://github.com/DLR-RM/stable-baselines3/issues/284\n ",
|
70 |
+
"__init__": "<function ReplayBuffer.__init__ at 0x7fe81086ab90>",
|
71 |
+
"add": "<function ReplayBuffer.add at 0x7fe81086ac20>",
|
72 |
+
"sample": "<function ReplayBuffer.sample at 0x7fe81086acb0>",
|
73 |
+
"_get_samples": "<function ReplayBuffer._get_samples at 0x7fe81086ad40>",
|
74 |
+
"_maybe_cast_dtype": "<staticmethod(<function ReplayBuffer._maybe_cast_dtype at 0x7fe81086add0>)>",
|
75 |
+
"__abstractmethods__": "frozenset()",
|
76 |
+
"_abc_impl": "<_abc._abc_data object at 0x7fe8109d1600>"
|
77 |
+
},
|
78 |
+
"replay_buffer_kwargs": {},
|
79 |
+
"train_freq": {
|
80 |
+
":type:": "<class 'stable_baselines3.common.type_aliases.TrainFreq'>",
|
81 |
+
":serialized:": "gAWVYQAAAAAAAACMJXN0YWJsZV9iYXNlbGluZXMzLmNvbW1vbi50eXBlX2FsaWFzZXOUjAlUcmFpbkZyZXGUk5RLAWgAjBJUcmFpbkZyZXF1ZW5jeVVuaXSUk5SMBHN0ZXCUhZRSlIaUgZQu"
|
82 |
+
},
|
83 |
+
"use_sde_at_warmup": false,
|
84 |
+
"target_entropy": -6.0,
|
85 |
+
"ent_coef": "auto",
|
86 |
+
"target_update_interval": 1,
|
87 |
+
"observation_space": {
|
88 |
+
":type:": "<class 'gymnasium.spaces.box.Box'>",
|
89 |
+
":serialized:": "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",
|
90 |
+
"dtype": "float64",
|
91 |
+
"bounded_below": "[False False False False False False False False False False False False\n False False False False False]",
|
92 |
+
"bounded_above": "[False False False False False False False False False False False False\n False False False False False]",
|
93 |
+
"_shape": [
|
94 |
+
17
|
95 |
+
],
|
96 |
+
"low": "[-inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf -inf\n -inf -inf -inf]",
|
97 |
+
"high": "[inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf inf]",
|
98 |
+
"low_repr": "-inf",
|
99 |
+
"high_repr": "inf",
|
100 |
+
"_np_random": null
|
101 |
+
},
|
102 |
+
"action_space": {
|
103 |
+
":type:": "<class 'gymnasium.spaces.box.Box'>",
|
104 |
+
":serialized:": "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",
|
105 |
+
"dtype": "float32",
|
106 |
+
"bounded_below": "[ True True True True True True]",
|
107 |
+
"bounded_above": "[ True True True True True True]",
|
108 |
+
"_shape": [
|
109 |
+
6
|
110 |
+
],
|
111 |
+
"low": "[-1. -1. -1. -1. -1. -1.]",
|
112 |
+
"high": "[1. 1. 1. 1. 1. 1.]",
|
113 |
+
"low_repr": "-1.0",
|
114 |
+
"high_repr": "1.0",
|
115 |
+
"_np_random": "Generator(PCG64)"
|
116 |
+
},
|
117 |
+
"n_envs": 5,
|
118 |
+
"lr_schedule": {
|
119 |
+
":type:": "<class 'function'>",
|
120 |
+
":serialized:": "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"
|
121 |
+
},
|
122 |
+
"batch_norm_stats": [],
|
123 |
+
"batch_norm_stats_target": []
|
124 |
+
}
|
walker2d-v5-sac-expert/ent_coef_optimizer.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:f4268548ce2d85a84d844693cdc5e124d00b10400478edf85c170526d53ec5f1
|
3 |
+
size 1507
|
walker2d-v5-sac-expert/policy.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:809e07f78a8d6532f0a80c3aca18c91c1487f248533b20fe091a4c7a69e071e1
|
3 |
+
size 1459973
|
walker2d-v5-sac-expert/pytorch_variables.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:a90854a67af348a7cbfa8c1fa493eb7f88c3f9bd6cf1ada0b14041f2145fccda
|
3 |
+
size 747
|
walker2d-v5-sac-expert/system_info.txt
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
- OS: Linux-6.2.0-35-generic-x86_64-with-glibc2.35 # 35~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Fri Oct 6 10:23:26 UTC 2
|
2 |
+
- Python: 3.10.12
|
3 |
+
- Stable-Baselines3: 2.1.0
|
4 |
+
- PyTorch: 2.0.1+cu117
|
5 |
+
- GPU Enabled: True
|
6 |
+
- Numpy: 1.26.1
|
7 |
+
- Cloudpickle: 2.2.1
|
8 |
+
- Gymnasium: 0.29.0
|
9 |
+
- OpenAI Gym: 0.23.1
|