Browse State-of-the-Art › reinforcement-learning

reinforcement-learning

4,119 papers with code · 0 benchmarks · 1 dataset 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

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

Subtasks archive 2025-07-28

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Most implemented papers archive 2025-07-28

30 shown of 4,119 papers with code (13,427 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.

  • 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)
  • 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.
  • 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…
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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
  • 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.
  • 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.
  • 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.
  • 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…
  • 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.
  • 29 May 2023 29 repositories listed Syntology ran 6 of 31 samples · 25 unverified · 2 pointer-only (licence)
    Existing methods for gaining such steerability collect human labels of the relative quality of model generations and fine-tune the unsupervised LM to align with these preferences, often with reinforcement learning from…
  • 26 Feb 2018 28 repositories listed
    The purpose of this technical report is two-fold.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 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.
  • 2 Mar 2021 19 repositories listed Syntology ran 2 of 2 samples · 0 unverified
    This is often due to the belief that PPO is significantly less sample efficient than off-policy methods in multi-agent systems.
  • 14 Jun 2018 19 repositories listed Syntology ran 0 of 2 samples · 2 unverified
    In this work, we build on recent advances in distributional reinforcement learning to give a generally applicable, flexible, and state-of-the-art distributional variant of DQN.
  • 9 Nov 2016 19 repositories listed Syntology ran 11 of 21 samples · 10 unverified
    The activations of the RNN store the state of the "fast" RL algorithm on the current (previously unseen) MDP.
  • 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.

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.

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