Reinforcement Learning (RL)

Description from the archive archive 2025-07-28.

Benchmarks archive 2025-07-28

2 leaderboard tables shown for this task, 2 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

20 datasets whose archive record lists this task, ordered by the archive's paper count.

Subtasks archive 2025-07-28

6 subtasks in the archive's task tree.

Most implemented papers archive 2025-07-28

30 shown of 4,749 papers with code (15,113 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.
• Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
  9 Mar 2017 85 repositories listed Syntology ran 86 of 154 samples · 68 unverified · 57 pointer-only (licence)
  We propose an algorithm for meta-learning that is model-agnostic, in the sense that it is compatible with any model trained with gradient descent and applicable to a variety of different learning problems, including…
• Prioritized Experience Replay
  18 Nov 2015 77 repositories listed Syntology ran 78 of 111 samples · 33 unverified · 43 pointer-only (licence)
  Experience replay lets online reinforcement learning agents remember and reuse experiences from the past.
• Dueling Network Architectures for Deep Reinforcement Learning
  20 Nov 2015 73 repositories listed Syntology ran 5 of 11 samples · 6 unverified · 6 pointer-only (licence)
  In recent years there have been many successes of using deep representations in reinforcement learning.
• Asynchronous Methods for Deep Reinforcement Learning
  4 Feb 2016 70 repositories listed Syntology ran 38 of 95 samples · 57 unverified · 12 pointer-only (licence)
  We propose a conceptually simple and lightweight framework for deep reinforcement learning that uses asynchronous gradient descent for optimization of deep neural network controllers.
• 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.
• Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm
  5 Dec 2017 62 repositories listed Syntology ran 13 of 17 samples · 4 unverified · 9 pointer-only (licence)
  The game of chess is the most widely-studied domain in the history of artificial intelligence.
• DARTS: Differentiable Architecture Search
  24 Jun 2018 59 repositories listed Syntology ran 66 of 156 samples · 90 unverified · 48 pointer-only (licence)
  This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner.
• Soft Actor-Critic Algorithms and Applications
  13 Dec 2018 52 repositories listed Syntology ran 10 of 40 samples · 30 unverified · 1 pointer-only (licence)
  A fork of OpenAI Baselines, implementations of reinforcement learning algorithms
• OpenAI Gym
  5 Jun 2016 45 repositories listed Syntology ran 2 of 2 samples · 0 unverified · 2 pointer-only (licence)
  OpenAI Gym is a toolkit for reinforcement learning research.
• Weight Uncertainty in Neural Networks
  20 May 2015 38 repositories listed Syntology ran 12 of 15 samples · 3 unverified · 7 pointer-only (licence)
  We introduce a new, efficient, principled and backpropagation-compatible algorithm for learning a probability distribution on the weights of a neural network, called Bayes by Backprop.
• Rainbow: Combining Improvements in Deep Reinforcement Learning
  6 Oct 2017 34 repositories listed Syntology ran 2 of 6 samples · 4 unverified · 1 pointer-only (licence)
  The deep reinforcement learning community has made several independent improvements to the DQN algorithm.
• Self-critical Sequence Training for Image Captioning
  2 Dec 2016 31 repositories listed Syntology ran 8 of 13 samples · 5 unverified · 3 pointer-only (licence)
  In this paper we consider the problem of optimizing image captioning systems using reinforcement learning, and show that by carefully optimizing our systems using the test metrics of the MSCOCO task, significant gains…
• A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
  30 Jun 2017 30 repositories listed
  They are, along with a number of recently reviewed or published portfolio-selection strategies, examined in three back-test experiments with a trading period of 30 minutes in a cryptocurrency market.
• Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
  26 Feb 2018 28 repositories listed
  The purpose of this technical report is two-fold.
• Hindsight Experience Replay
  5 Jul 2017 28 repositories listed Syntology ran 16 of 16 samples · 0 unverified · 9 pointer-only (licence)
  Dealing with sparse rewards is one of the biggest challenges in Reinforcement Learning (RL).
• Simple random search provides a competitive approach to reinforcement learning
  19 Mar 2018 26 repositories listed Syntology ran 3 of 15 samples · 12 unverified · 3 pointer-only (licence)
  A common belief in model-free reinforcement learning is that methods based on random search in the parameter space of policies exhibit significantly worse sample complexity than those that explore the space of actions.
• IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures
  5 Feb 2018 24 repositories listed Syntology ran 16 of 34 samples · 18 unverified · 3 pointer-only (licence)
  In this work we aim to solve a large collection of tasks using a single reinforcement learning agent with a single set of parameters.
• The StarCraft Multi-Agent Challenge
  11 Feb 2019 23 repositories listed Syntology ran 6 of 15 samples · 9 unverified · 13 pointer-only (licence)
  In this paper, we propose the StarCraft Multi-Agent Challenge (SMAC) as a benchmark problem to fill this gap.
• ParlAI: A Dialog Research Software Platform
  18 May 2017 23 repositories listed
  We introduce ParlAI (pronounced "par-lay"), an open-source software platform for dialog research implemented in Python, available at http://parl.
• 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.
• SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
  18 Sep 2016 23 repositories listed Syntology ran 10 of 21 samples · 11 unverified · 13 pointer-only (licence)
  As a new way of training generative models, Generative Adversarial Nets (GAN) that uses a discriminative model to guide the training of the generative model has enjoyed considerable success in generating real-valued…
• Exploration by Random Network Distillation
  30 Oct 2018 22 repositories listed Syntology ran 26 of 43 samples · 17 unverified · 15 pointer-only (licence)
  In particular we establish state of the art performance on Montezuma's Revenge, a game famously difficult for deep reinforcement learning methods.
• World Models
  27 Mar 2018 22 repositories listed Syntology ran 6 of 38 samples · 32 unverified · 3 pointer-only (licence)
  We explore building generative neural network models of popular reinforcement learning environments.
• A Distributional Perspective on Reinforcement Learning
  21 Jul 2017 22 repositories listed Syntology ran 0 of 1 samples · 1 unverified · 1 pointer-only (licence)
  We obtain both state-of-the-art results and anecdotal evidence demonstrating the importance of the value distribution in approximate reinforcement learning.
• Dream to Control: Learning Behaviors by Latent Imagination
  3 Dec 2019 21 repositories listed Syntology ran 43 of 62 samples · 19 unverified · 9 pointer-only (licence)
  Learned world models summarize an agent's experience to facilitate learning complex behaviors.

Syntology lines on 27 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.