Papers › Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a...
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Tuomas Haarnoja, Aurick Zhou, Pieter Abbeel, Sergey Levine
A platform for Applied Reinforcement Learning (Applied RL)
In Syntology Open this paper in Syntology's Atlas, the map of the papers in Syntology's graph and their citations.
For agents, Syntology's MCP tool lists every function and class Syntology harvested from this paper and whether it ran (how to connect): get_harvested_code_for_paper(arxiv_id="1801.01290")
Code
Syntology Ran 91 of 148 code samples harvested from 27 repositories linked to this paper; 57 have no recorded run. Of those that ran: 3 ran · honoured contract; 1 ran · violated contract; 17 ran · our draft was wrong; 1 ran · fixture could not drive it; 69 ran with no contract checked.
By repository: community (archive-listed): 148 samples from 27 repositories, 91 ran. The run record, sample by sample. “Ran” means executed on a synthesized input, not that the code is correct or reproduces the paper.
86 repositories listed; official and paper-mentioned ones first.
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
148 samples harvested; 91 ran; 3 honoured the contract we drafted; 57 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
Licence: 66 of the 148 samples are pointer only, meaning Syntology does not serve that copy's text. This page shows no code text for any sample; each one links to its file in the repository.
Harvested from 27 repositories linked to this paper, official or community; each sample names its own and says which. “Ran” means the sample executed on a synthesized input. It does not mean the output is correct, and nothing here reproduces the paper's results. “Honoured” and “violated” refer to a contract Syntology drafted from the code itself; “our draft was wrong” and “fixture could not drive it” are failures of Syntology's instrument, not of the code.
Each sample ends with its code_sha256, Syntology's identity for that exact code. An agent fetches the stored sample with Syntology's MCP tool get_code(code_sha256="…") (how to connect); click an identity to copy that call.
Repository labels, per sample. official repository: The archive marks this repository official for the paper. named in the paper: The archive records that the paper mentions this repository; it is not marked official. community (archive-listed): In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper. found in paper text by Syntology: Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted. community: Not in the archive's code links for this paper; a community repository Syntology harvested. Samples from a repository marked official are listed first. Licence labels name the repository's licence as recorded at harvest. “Pointer only” means Syntology does not serve that copy's text, for one of four reasons: no licence file was found; the licence was not identified; the licence is recorded as permissive but that copy's record is not marked cleared; or the licence is outside the permissive list Syntology serves text under (MIT, Apache-2.0, BSD and similar). Some licences outside that list permit redistribution, such as WTFPL, and GPL-3.0 under its conditions; they are simply not on the list. Hover a licence label for the reason. File links open the file on GitHub at the default branch, which may have changed since the harvest.
424bf06f8d8eb998 · report
de5e64dc2bfc6a33 · report
5d8f81c210a0aacb · report
dc01224a73ea8148 · report
0918147b61a18074 · report
d6b54769dcaa3ec5 · report
6bb425a4771dc88f · report
e6f3bef6186ec524 · report
94573a894a42ebff · report
c10734f04f231cb4 · report
7c4433b21cf61a2c · report
f8ead4c2c205ecd8 · report
8f0377d2cad944f3 · report
a0a4c6c892e497cd · report
9ee95d5b4fc2dd44 · report
a57bb45ab6a0bc51 · report
693c33a1aa6474c6 · report
7553f61c31debefb · report
3faeea6e22edd34d · report
d4dfbbf39952f704 · report
bf0d89394b22e1b6 · report
152bc1e449547d23 · report
8761dc745b44dc04 · report
8ee15fddb3528e86 · report
da8ebd6e9ea11fca · report
5722a85a4a153169 · report
57c7562fda9bbab3 · report
94a135edcb95baf2 · report
4aec8353051fa16d · report
a830c8ece63f9c57 · report
c02fc6e11c3ba5cf · report
14211bf5685457cf · report
5d3a98fbf8446e0c · report
de764e9177884a12 · report
540316788d2c30c7 · report
ec7050db6c32a43a · report
91bcdaabd64f8eee · report
f37748964b0cd7da · report
b7030d8d56cd639a · report
0d80189fd77d3aa6 · report
e3aca820c0049604 · report
e182a5c8f1d1e16f · report
ac5ecca5b7705f8a · report
8119b0afbe2d940e · report
a6c39d1200737d3f · report
e9664791458e5ade · report
70249df825c62d7e · report
0020d85f4dbfb10b · report
217cd7f6f86864da · report
43d63d9b3b6240a1 · report
e5d43066162708ff · report
33ec92b652664841 · report
c74fba5d30037b69 · report
eb9bc09b3ca641cb · report
b473c449b3da39cc · report
0688d4da92feee38 · report
02a45597a277afab · report
b1e066bc1ff4b61b · report
f0f9d0b00b3e9632 · report
77baa7a1a168fc24 · report
7e61ac4f70c90628 · report
f552f5cc13186908 · report
5b2e9a4c85bbab7d · report
5d13132d2e3c5ef0 · report
e42d91ba746c3a14 · report
f8845c35735f20bc · report
18ecb1311366b8cb · report
b7fc01f37cbb0eb1 · report
52028903a218ba91 · report
fa8517ef05057c7f · report
d9d455622c2dcd61 · report
4731ca1d377b8284 · report
a2697a9ea209c680 · report
cdf94e91c8746537 · report
248f63c9cf4d972e · report
36b66092679a3132 · report
298e84cf1d9ed36c · report
5c26c93931dce503 · report
958cab9f3ba6af81 · report
d4978950888874f4 · report
1afbe631504fbaf7 · report
1dd9b9ae9fa9426f · report
c70b5621a0ca8047 · report
ad86f46cc9af956b · report
a1e9dc4773ffeba5 · report
5c5bce3523ed4d69 · report
1d75daad61f15424 · report
642892e9419a125d · report
4a486e5048778576 · report
3a0835e6ce008741 · report
5a4d3fd6b28ecaf4 · report
b0e759b2d521e146 · report
2ae117303c9ac594 · report
05437b66f08eb4d6 · report
eaf547baf5f33e43 · report
803d90cd603ff8c0 · report
ff7a80131e64a08d · report
110041979f3c0cb8 · report
d6d4b5a5d84ba8ad · report
b6b1a23b07a627dd · report
3ef56075bad93ec9 · report
7524ef9f7598dd6f · report
b36d35fd411b0150 · report
995a5d6382ee476d · report
569d921eb9b51536 · report
0edd4b0f90be8f84 · report
4849ba61ae47dd70 · report
348b1924f106de50 · report
9e2e2c85eabbd88f · report
1cb5a8aa4ee6cb43 · report
9efbd1e63c6644e5 · report
6af70c8a2239d7c5 · report
fa07dbb92f6c222b · report
104ef2d3e37baa21 · report
d77f61cb28da8383 · report
6bc96035de025e83 · report
3e1e70dd1d5f3958 · report
ecf34b65e8d9d3bb · report
231f633be51bd617 · report
f07528bc5e071757 · report
198be549ce9696ef · report
ded76544fe9d6607 · report
d1076220e33b55ba · report
ed542fae5c7f2c04 · report
799c08d64f58d9ef · report
7abe222eb3afeb6f · report
1adf91cc413cf6c2 · report
8576d4b41e6ed12f · report
544fe1f08a36cc62 · report
706459e26fbb0b4c · report
e68b07bcdded00fa · report
8498689f07030ac8 · report
a860fee8188a0218 · report
f299a2900b4034fa · report
7d297778525c40d1 · report
0dfad75cd2d202d6 · report
f735812881238a1f · report
e8abbe917721f0c8 · report
fcdeb82694a504e1 · report
8dc0540b8dfe9b97 · report
ace24484ab1d6b7f · report
54ab8d446018b958 · report
f62b5d3c6983e5aa · report
85fff8ae028fc1dd · report
68c08ef9d5d473a7 · report
6ae1b395aeb4e7d3 · report
0537c4bc8300d410 · report
abe2b5a0b5e817fd · report
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Continuous Control | Lunar Lander (OpenAI Gym) | SAC | Score | 284.59±0.97 | #1 of 5 | Archive leaderboard | report |
| OpenAI Gym | Ant-v4 | SAC | Average Return | 5208.09 | #3 of 5 | Archive leaderboard | report |
| OpenAI Gym | HalfCheetah-v4 | SAC | Average Return | 15836.04 | #1 of 5 | Archive leaderboard | report |
| OpenAI Gym | Hopper-v4 | SAC | Average Return | 2882.56 | #3 of 5 | Archive leaderboard | report |
| OpenAI Gym | Humanoid-v4 | SAC | Average Return | 6211.50 | #2 of 5 | Archive leaderboard | report |
| OpenAI Gym | Walker2d-v4 | SAC | Average Return | 5745.27 | #1 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: Soft Actor Critic
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections