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 1726–1750 of 15113 papers

TitleStatusHype
On the Robustness of Cooperative Multi-Agent Reinforcement LearningCode1
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
Combinatorial Optimization with Policy Adaptation using Latent Space SearchCode1
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
Combining Reinforcement Learning with Model Predictive Control for On-Ramp MergingCode1
Agent Modelling under Partial Observability for Deep Reinforcement LearningCode1
Tactile Sim-to-Real Policy Transfer via Real-to-Sim Image TranslationCode1
Optimal Energy System Scheduling Using A Constraint-Aware Reinforcement Learning AlgorithmCode1
Optimal Goal-Reaching Reinforcement Learning via Quasimetric LearningCode1
A Learning System for Motion Planning of Free-Float Dual-Arm Space Manipulator towards Non-Cooperative ObjectCode1
Bayesian Action Decoder for Deep Multi-Agent Reinforcement LearningCode1
Optimistic Exploration even with a Pessimistic InitialisationCode1
Single-step deep reinforcement learning for open-loop control of laminar and turbulent flowsCode1
Optimization of the Model Predictive Control Update Interval Using Reinforcement LearningCode1
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley ValuesCode1
Optimizing Large-Scale Fleet Management on a Road Network using Multi-Agent Deep Reinforcement Learning with Graph Neural NetworkCode1
Optimizing Quantum Variational Circuits with Deep Reinforcement LearningCode1
Making Offline RL Online: Collaborative World Models for Offline Visual Reinforcement LearningCode1
Stable and Safe Reinforcement Learning via a Barrier-Lyapunov Actor-Critic ApproachCode1
ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization ProblemsCode1
OstrichRL: A Musculoskeletal Ostrich Simulation to Study Bio-mechanical LocomotionCode1
PAC Confidence Sets for Deep Neural Networks via Calibrated PredictionCode1
PaCo: Parameter-Compositional Multi-Task Reinforcement LearningCode1
PantheonRL: A MARL Library for Dynamic Training InteractionsCode1
Collision Probability Distribution Estimation via Temporal Difference LearningCode1
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Benchmark Results

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