SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1701–1725 of 15113 papers

TitleStatusHype
Augmenting Reinforcement Learning with Behavior Primitives for Diverse Manipulation TasksCode1
Augmenting Reinforcement Learning with Transformer-based Scene Representation Learning for Decision-making of Autonomous DrivingCode1
Offline Reinforcement Learning with Value-based Episodic MemoryCode1
A Unified Approach to Reinforcement Learning, Quantal Response Equilibria, and Two-Player Zero-Sum GamesCode1
Offline RL with No OOD Actions: In-Sample Learning via Implicit Value RegularizationCode1
Offline RL Without Off-Policy EvaluationCode1
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
Off-Policy General Value Functions to Represent Dynamic Role Assignments in RoboCup 3D Soccer SimulationCode1
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
Procedural generation of meta-reinforcement learning tasksCode1
One Policy to Control Them All: Shared Modular Policies for Agent-Agnostic ControlCode1
A Large Recurrent Action Model: xLSTM enables Fast Inference for Robotics TasksCode1
Provably Efficient Online Hyperparameter Optimization with Population-Based BanditsCode1
On Learning Paradigms for the Travelling Salesman ProblemCode1
Online 3D Bin Packing with Constrained Deep Reinforcement LearningCode1
Online Anomalous Subtrajectory Detection on Road Networks with Deep Reinforcement LearningCode1
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley ValuesCode1
AlberDICE: Addressing Out-Of-Distribution Joint Actions in Offline Multi-Agent RL via Alternating Stationary Distribution Correction EstimationCode1
Online Symbolic Music Alignment with Offline Reinforcement LearningCode1
Alchemy: A benchmark and analysis toolkit for meta-reinforcement learning agentsCode1
On Penalty-based Bilevel Gradient Descent MethodCode1
On Simple Reactive Neural Networks for Behaviour-Based Reinforcement LearningCode1
On the Feasibility of Cross-Task Transfer with Model-Based Reinforcement LearningCode1
Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement LearningCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified