D4RL

Benchmarks archive 2025-07-28

1 leaderboard table shown for this task, 1 with rows (a “benchmark” on this site is a table with at least one row, as on /sota), ordered by row count. “Best model” is the first row in the archive's own order at snapshot; nothing is re-ranked here and metric direction is not recorded in the archive. PwC's Trend sparklines are not in the archive, so that column is omitted.

Syntology column: samples harvested from the paper's repositories and executed on synthesized fixtures; “ran” is not a correctness claim and does not order the table. A dash means no Syntology record for that paper, not a recorded non-run. Read from the graph 2026-09-24.

Libraries

Not in the archive: the export carries no per-task library table, so there is nothing to show at snapshot 2025-07-28.

Datasets archive 2025-07-28

1 dataset whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

No subtask under this task in the archive's task tree.

Parent tasks archive 2025-07-28

Most implemented papers archive 2025-07-28

30 shown of 108 papers with code (226 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• Decision Transformer: Reinforcement Learning via Sequence Modeling
  2 Jun 2021 20 repositories listed Syntology ran 17 of 26 samples · 9 unverified · 6 pointer-only (licence)
  In particular, we present Decision Transformer, an architecture that casts the problem of RL as conditional sequence modeling.
• Offline Reinforcement Learning with Implicit Q-Learning
  12 Oct 2021 17 repositories listed Syntology ran 32 of 58 samples · 26 unverified · 22 pointer-only (licence)
  The main insight in our work is that, instead of evaluating unseen actions from the latest policy, we can approximate the policy improvement step implicitly by treating the state value function as a random variable,…
• Reformer: The Efficient Transformer
  13 Jan 2020 10 repositories listed Syntology ran 6 of 8 samples · 2 unverified
  Large Transformer models routinely achieve state-of-the-art results on a number of tasks but training these models can be prohibitively costly, especially on long sequences.
• Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention
  29 Jun 2020 8 repositories listed Syntology ran 3 of 8 samples · 5 unverified · 3 pointer-only (licence)
  Transformers achieve remarkable performance in several tasks but due to their quadratic complexity, with respect to the input's length, they are prohibitively slow for very long sequences.
• Rethinking Attention with Performers
  30 Sep 2020 7 repositories listed Syntology ran 9 of 16 samples · 7 unverified · 6 pointer-only (licence)
  We introduce Performers, Transformer architectures which can estimate regular (softmax) full-rank-attention Transformers with provable accuracy, but using only linear (as opposed to quadratic) space and time complexity,…
• D4RL: Datasets for Deep Data-Driven Reinforcement Learning
  15 Apr 2020 7 repositories listed Syntology ran 4 of 12 samples · 8 unverified
  In this work, we introduce benchmarks specifically designed for the offline setting, guided by key properties of datasets relevant to real-world applications of offline RL.
• CORL: Research-oriented Deep Offline Reinforcement Learning Library
  13 Oct 2022 5 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  CORL is an open-source library that provides thoroughly benchmarked single-file implementations of both deep offline and offline-to-online reinforcement learning algorithms.
• Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble
  4 Oct 2021 5 repositories listed Syntology ran 13 of 21 samples · 8 unverified · 6 pointer-only (licence)
  However, prior methods typically require accurate estimation of the behavior policy or sampling from OOD data points, which themselves can be a non-trivial problem.
• Implicit Behavioral Cloning
  1 Sep 2021 5 repositories listed Syntology ran 2 of 2 samples · 0 unverified
  We find that across a wide range of robot policy learning scenarios, treating supervised policy learning with an implicit model generally performs better, on average, than commonly used explicit models.
• Offline RL with No OOD Actions: In-Sample Learning via Implicit Value Regularization
  28 Mar 2023 4 repositories listed Syntology ran 1 of 3 samples · 2 unverified
  This gives a deeper understanding of why the in-sample learning paradigm works, i.
• Extreme Q-Learning: MaxEnt RL without Entropy
  5 Jan 2023 4 repositories listed Syntology ran 8 of 13 samples · 5 unverified · 7 pointer-only (licence)
  Using EVT, we derive our \emph{Extreme Q-Learning} framework and consequently online and, for the first time, offline MaxEnt Q-learning algorithms, that do not explicitly require access to a policy or its entropy.
• Datasets and Benchmarks for Offline Safe Reinforcement Learning
  15 Jun 2023 3 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  This paper presents a comprehensive benchmarking suite tailored to offline safe reinforcement learning (RL) challenges, aiming to foster progress in the development and evaluation of safe learning algorithms in both the…
• Revisiting the Minimalist Approach to Offline Reinforcement Learning
  16 May 2023 3 repositories listed Syntology ran 7 of 16 samples · 9 unverified
  Recent years have witnessed significant advancements in offline reinforcement learning (RL), resulting in the development of numerous algorithms with varying degrees of complexity.
• Contrastive Energy Prediction for Exact Energy-Guided Diffusion Sampling in Offline Reinforcement Learning
  25 Apr 2023 3 repositories listed Syntology ran 17 of 27 samples · 10 unverified · 5 pointer-only (licence)
  The main challenge for this setting is that the intermediate guidance during the diffusion sampling procedure, which is jointly defined by the sampling distribution and the energy function, is unknown and is hard to…
• Anti-Exploration by Random Network Distillation
  31 Jan 2023 3 repositories listed Syntology ran 20 of 26 samples · 6 unverified · 7 pointer-only (licence)
  Despite the success of Random Network Distillation (RND) in various domains, it was shown as not discriminative enough to be used as an uncertainty estimator for penalizing out-of-distribution actions in offline…
• Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief
  13 Oct 2022 3 repositories listed
  To make practical, we further devise an offline RL algorithm to approximately find the solution.
• Diffusion Policies as an Expressive Policy Class for Offline Reinforcement Learning
  12 Aug 2022 3 repositories listed Syntology ran 11 of 18 samples · 7 unverified · 10 pointer-only (licence)
  In our approach, we learn an action-value function and we add a term maximizing action-values into the training loss of the conditional diffusion model, which results in a loss that seeks optimal actions that are near…
• Mildly Conservative Q-Learning for Offline Reinforcement Learning
  9 Jun 2022 3 repositories listed Syntology ran 12 of 16 samples · 4 unverified · 6 pointer-only (licence)
  The distribution shift between the learned policy and the behavior policy makes it necessary for the value function to stay conservative such that out-of-distribution (OOD) actions will not be severely overestimated.
• cosFormer: Rethinking Softmax in Attention
  17 Feb 2022 3 repositories listed
  As one of its core components, the softmax attention helps to capture long-range dependencies yet prohibits its scale-up due to the quadratic space and time complexity to the sequence length.
• Adversarially Trained Actor Critic for Offline Reinforcement Learning
  5 Feb 2022 3 repositories listed Syntology ran 3 of 3 samples · 0 unverified
  We propose Adversarially Trained Actor Critic (ATAC), a new model-free algorithm for offline reinforcement learning (RL) under insufficient data coverage, based on the concept of relative pessimism.
• Flow Q-Learning
  4 Feb 2025 2 repositories listed Syntology ran 5 of 7 samples · 2 unverified · 5 pointer-only (licence)
  We present flow Q-learning (FQL), a simple and performant offline reinforcement learning (RL) method that leverages an expressive flow-matching policy to model arbitrarily complex action distributions in data.
• Q-value Regularized Transformer for Offline Reinforcement Learning
  27 May 2024 2 repositories listed Syntology ran 2 of 8 samples · 6 unverified
  Recent advancements in offline reinforcement learning (RL) have underscored the capabilities of Conditional Sequence Modeling (CSM), a paradigm that learns the action distribution based on history trajectory and target…
• Decision Mamba Architectures
  13 May 2024 2 repositories listed
  In this work, we introduce two novel methods, Decision Mamba (DM) and Hierarchical Decision Mamba (HDM), aimed at enhancing the performance of the Transformer models.
• Exploration and Anti-Exploration with Distributional Random Network Distillation
  18 Jan 2024 2 repositories listed Syntology ran 4 of 4 samples · 0 unverified
  To address this issue, we introduce the Distributional RND (DRND), a derivative of the RND.
• Model-Bellman Inconsistency for Model-based Offline Reinforcement Learning
  1 Jul 2023 2 repositories listed
  MOBILE conducts uncertainty quantification through the inconsistency of Bellman estimations under an ensemble of learned dynamics models, which can be a better approximator to the true Bellman error, and penalizes the…
• Behavior Proximal Policy Optimization
  22 Feb 2023 2 repositories listed Syntology ran 2 of 3 samples · 1 unverified
  Offline reinforcement learning (RL) is a challenging setting where existing off-policy actor-critic methods perform poorly due to the overestimation of out-of-distribution state-action pairs.
• When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning
  15 Feb 2023 2 repositories listed
  Offline inverse reinforcement learning (Offline IRL) aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demonstrations from an expert agent.
• Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement Learning
  25 Oct 2022 2 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  Offline reinforcement learning, by learning from a fixed dataset, makes it possible to learn agent behaviors without interacting with the environment.
• Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters
  27 May 2022 2 repositories listed
  Motivated by the success of ensembles for uncertainty estimation in supervised learning, we take a renewed look at how ensembles of Q-functions can be leveraged as the primary source of pessimism for offline…
• When Data Geometry Meets Deep Function: Generalizing Offline Reinforcement Learning
  23 May 2022 2 repositories listed
  In offline reinforcement learning (RL), one detrimental issue to policy learning is the error accumulation of deep Q function in out-of-distribution (OOD) areas.

Syntology lines on 23 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. Read from the graph 2026-09-24.