Browse State-of-the-Art › D4RL › Papers, page 3
D4RL
Papers archive 2025-07-28
archive papers tagged: 226 · with a code link: 108 · where Syntology ran a sample: 68 (59 with a run with no instrument failure, 9 where every run was a failure of Syntology's instrument) Syntology
Show: all tagged papersonly where code ran (68 of 226 tagged: 59 with a run with no instrument failure, 9 where every run was a failure of Syntology's instrument)
Page 3 of 3: papers 201 to 226 of 226, in archive order: by repositories listed in the archive (most first), then newest first, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers that list no repository come after every paper that lists one.
Papers without a page here are shown as plain text. A Syntology line reads “N ran (of which C constructed an object rather than computing a result; K with no instrument failure: H honoured, V violated, P with no contract checked; I where Syntology's instrument failed) · U unverified”; the figure “where Syntology's instrument failed” counts failures of Syntology's instrument, not of the code. When the archive marks a repository official for the paper, the line starts with that repository's state (the archive's flag, not a verdict on who wrote the code); hover it for the repositories the samples that ran came from. Abstracts are on each paper's page.
-
Boosting Offline Reinforcement Learning via Data Rebalancing17 Oct 2022 0 repositories listed
-
DCE: Offline Reinforcement Learning With Double Conservative Estimates27 Sep 2022 0 repositories listed
-
Hierarchical Decision Transformer21 Sep 2022 0 repositories listed
-
Addressing Optimism Bias in Sequence Modeling for Reinforcement Learning21 Jul 2022 0 repositories listed
-
On the Role of Discount Factor in Offline Reinforcement Learning7 Jun 2022 0 repositories listed
-
Know Your Boundaries: The Necessity of Explicit Behavioral Cloning in Offline RL1 Jun 2022 0 repositories listed
-
A Behavior Regularized Implicit Policy for Offline Reinforcement Learning19 Feb 2022 0 repositories listed
-
MOORe: Model-based Offline-to-Online Reinforcement Learning25 Jan 2022 0 repositories listed
-
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization9 Dec 2021 0 repositories listed
-
Quantile Filtered Imitation Learning2 Dec 2021 0 repositories listed
-
2 Nov 2021 0 repositories listed
-
Offline RL With Resource Constrained Online Deployment7 Oct 2021 0 repositories listed
-
You Only Evaluate Once: a Simple Baseline Algorithm for Offline RL5 Oct 2021 0 repositories listed
-
Offline Reinforcement Learning with Resource Constrained Online Deployment29 Sep 2021 0 repositories listed
-
Pareto Policy Pool for Model-based Offline Reinforcement Learning29 Sep 2021 0 repositories listed
-
Semi-supervised Offline Reinforcement Learning with Pre-trained Decision Transformers29 Sep 2021 0 repositories listed
-
State-Action Joint Regularized Implicit Policy for Offline Reinforcement Learning29 Sep 2021 0 repositories listed
-
Uncertainty Regularized Policy Learning for Offline Reinforcement Learning29 Sep 2021 0 repositories listed
-
Why so pessimistic? Estimating uncertainties for offline RL through ensembles, and why their independence matters.29 Sep 2021 0 repositories listed
-
Behavioral Priors and Dynamics Models: Improving Performance and Domain Transfer in Offline RL16 Jun 2021 0 repositories listed
-
S4RL: Surprisingly Simple Self-Supervision for Offline Reinforcement Learning10 Mar 2021 0 repositories listed
-
Reducing Conservativeness Oriented Offline Reinforcement Learning27 Feb 2021 0 repositories listed
-
Addressing Distribution Shift in Online Reinforcement Learning with Offline Datasets1 Jan 2021 0 repositories listed
-
Fine-Tuning Offline Reinforcement Learning with Model-Based Policy Optimization1 Jan 2021 0 repositories listed
-
EMaQ: Expected-Max Q-Learning Operator for Simple Yet Effective Offline and Online RL21 Jul 2020 0 repositories listed
Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced; each line links to that paper's sample list. Syntology's record for this page has not changed since , the first build that kept a record date for it; when this build read Syntology's graph is in the build record. For agents: get_harvested_code_for_paper(arxiv_id) lists each paper's samples; how to connect.