Q-Learning

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

No benchmark for this task in the archive.

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

2 datasets 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.

Most implemented papers archive 2025-07-28

30 shown of 463 papers with code (1,918 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.

• Continuous control with deep reinforcement learning
  9 Sep 2015 161 repositories listed Syntology ran 158 of 306 samples · 148 unverified · 163 pointer-only (licence)
  We adapt the ideas underlying the success of Deep Q-Learning to the continuous action domain.
• Playing Atari with Deep Reinforcement Learning
  19 Dec 2013 112 repositories listed Syntology ran 56 of 117 samples · 61 unverified · 56 pointer-only (licence)
  We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning.
• Deep Reinforcement Learning with Double Q-learning
  22 Sep 2015 97 repositories listed Syntology ran 55 of 106 samples · 51 unverified · 57 pointer-only (licence)
  The popular Q-learning algorithm is known to overestimate action values under certain conditions.
• Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
  4 Jan 2018 86 repositories listed Syntology ran 91 of 148 samples · 57 unverified · 66 pointer-only (licence)
  A platform for Applied Reinforcement Learning (Applied RL)
• Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
  7 Jun 2017 86 repositories listed Syntology ran 75 of 143 samples · 68 unverified · 99 pointer-only (licence)
  We explore deep reinforcement learning methods for multi-agent domains.
• Addressing Function Approximation Error in Actor-Critic Methods
  26 Feb 2018 67 repositories listed Syntology ran 9 of 36 samples · 27 unverified · 20 pointer-only (licence)
  In value-based reinforcement learning methods such as deep Q-learning, function approximation errors are known to lead to overestimated value estimates and suboptimal policies.
• Evolution Strategies as a Scalable Alternative to Reinforcement Learning
  10 Mar 2017 23 repositories listed Syntology ran 7 of 29 samples · 22 unverified · 1 pointer-only (licence)
  We explore the use of Evolution Strategies (ES), a class of black box optimization algorithms, as an alternative to popular MDP-based RL techniques such as Q-learning and Policy Gradients.
• Conservative Q-Learning for Offline Reinforcement Learning
  8 Jun 2020 18 repositories listed Syntology ran 24 of 34 samples · 10 unverified · 5 pointer-only (licence)
  We theoretically show that CQL produces a lower bound on the value of the current policy and that it can be incorporated into a policy learning procedure with theoretical improvement guarantees.
• 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,…
• A disembodied developmental robotic agent called Samu Bátfai
  9 Nov 2015 15 repositories listed
  The basic objective of this paper is to reach the same results using reinforcement learning with general function approximators that can be achieved by using the classical Q lookup table on small input samples.
• Deep Neuroevolution: Genetic Algorithms Are a Competitive Alternative for Training Deep Neural Networks for Reinforcement Learning
  18 Dec 2017 12 repositories listed Syntology ran 4 of 5 samples · 1 unverified · 4 pointer-only (licence)
  Here we demonstrate they can: we evolve the weights of a DNN with a simple, gradient-free, population-based genetic algorithm (GA) and it performs well on hard deep RL problems, including Atari and humanoid locomotion.
• ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning
  6 May 2016 10 repositories listed Syntology ran 2 of 5 samples · 3 unverified · 5 pointer-only (licence)
  Here, we propose a novel test-bed platform for reinforcement learning research from raw visual information which employs the first-person perspective in a semi-realistic 3D world.
• rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorch
  3 Sep 2019 9 repositories listed Syntology ran 0 of 10 samples · 10 unverified
  rlpyt is designed as a high-throughput code base for small- to medium-scale research in deep RL.
• Continuous Deep Q-Learning with Model-based Acceleration
  2 Mar 2016 8 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  In this paper, we explore algorithms and representations to reduce the sample complexity of deep reinforcement learning for continuous control tasks.
• Optimization of Molecules via Deep Reinforcement Learning
  19 Oct 2018 7 repositories listed
  We present a framework, which we call Molecule Deep Q-Networks (MolDQN), for molecule optimization by combining domain knowledge of chemistry and state-of-the-art reinforcement learning techniques (double Q-learning and…
• Playing FPS Games with Deep Reinforcement Learning
  18 Sep 2016 7 repositories listed
  Advances in deep reinforcement learning have allowed autonomous agents to perform well on Atari games, often outperforming humans, using only raw pixels to make their decisions.
• Randomized Ensembled Double Q-Learning: Learning Fast Without a Model
  15 Jan 2021 6 repositories listed Syntology ran 4 of 5 samples · 1 unverified · 3 pointer-only (licence)
  Using a high Update-To-Data (UTD) ratio, model-based methods have recently achieved much higher sample efficiency than previous model-free methods for continuous-action DRL benchmarks.
• QPLEX: Duplex Dueling Multi-Agent Q-Learning
  3 Aug 2020 6 repositories listed Syntology ran 2 of 4 samples · 2 unverified
  This paper presents a novel MARL approach, called duPLEX dueling multi-agent Q-learning (QPLEX), which takes a duplex dueling network architecture to factorize the joint value function.
• DeepTraffic: Crowdsourced Hyperparameter Tuning of Deep Reinforcement Learning Systems for Multi-Agent Dense Traffic Navigation
  9 Jan 2018 6 repositories listed
  We present a traffic simulation named DeepTraffic where the planning systems for a subset of the vehicles are handled by a neural network as part of a model-free, off-policy reinforcement learning process.
• Deep Q-learning from Demonstrations
  12 Apr 2017 6 repositories listed
  We present an algorithm, Deep Q-learning from Demonstrations (DQfD), that leverages small sets of demonstration data to massively accelerate the learning process even from relatively small amounts of demonstration data…
• 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.
• IQ-Learn: Inverse soft-Q Learning for Imitation
  23 Jun 2021 5 repositories listed Syntology ran 2 of 3 samples · 1 unverified · 3 pointer-only (licence)
  In many sequential decision-making problems (e.
• Benchmarking Batch Deep Reinforcement Learning Algorithms
  3 Oct 2019 5 repositories listed
  Widely-used deep reinforcement learning algorithms have been shown to fail in the batch setting--learning from a fixed data set without interaction with the environment.
• SQIL: Imitation Learning via Reinforcement Learning with Sparse Rewards
  27 May 2019 5 repositories listed
  Theoretically, we show that SQIL can be interpreted as a regularized variant of BC that uses a sparsity prior to encourage long-horizon imitation.
• Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
  28 Feb 2017 5 repositories listed
  Many real-world problems, such as network packet routing and urban traffic control, are naturally modeled as multi-agent reinforcement learning (RL) problems.
• Designing Neural Network Architectures using Reinforcement Learning
  7 Nov 2016 5 repositories listed Syntology ran 0 of 3 samples · 3 unverified
  We introduce MetaQNN, a meta-modeling algorithm based on reinforcement learning to automatically generate high-performing CNN architectures for a given learning task.
• Deep Recurrent Q-Learning for Partially Observable MDPs
  23 Jul 2015 5 repositories listed Syntology ran 0 of 2 samples · 2 unverified
  Deep Reinforcement Learning has yielded proficient controllers for complex tasks.
• Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition
  21 May 1999 5 repositories listed
  The paper presents an online model-free learning algorithm, MAXQ-Q, and proves that it converges wih probability 1 to a kind of locally-optimal policy known as a recursively optimal policy, even in the presence of the…
• 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.

Syntology lines on 21 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.