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

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Showing 2026–2050 of 15113 papers

TitleStatusHype
Learning When and Where to Zoom with Deep Reinforcement LearningCode1
Analysis of diversity-accuracy tradeoff in image captioningCode1
Optimistic Exploration even with a Pessimistic InitialisationCode1
Using Reinforcement Learning in the Algorithmic Trading ProblemCode1
Whole-Body Control of a Mobile Manipulator using End-to-End Reinforcement LearningCode1
Rewriting History with Inverse RL: Hindsight Inference for Policy ImprovementCode1
Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement LearningCode1
Discriminative Particle Filter Reinforcement Learning for Complex Partial ObservationsCode1
Reinforcement Learning Framework for Deep Brain Stimulation StudyCode1
How To Avoid Being Eaten By a Grue: Exploration Strategies for Text-Adventure AgentsCode1
Sim2Real Transfer for Reinforcement Learning without Dynamics RandomizationCode1
Reinforcement Learning for Molecular Design Guided by Quantum MechanicsCode1
Generating Automatic Curricula via Self-Supervised Active Domain RandomizationCode1
Kalman meets Bellman: Improving Policy Evaluation through Value TrackingCode1
R-MADDPG for Partially Observable Environments and Limited CommunicationCode1
Reinforced active learning for image segmentationCode1
PDDLGym: Gym Environments from PDDL ProblemsCode1
Deep RL Agent for a Real-Time Action Strategy GameCode1
An Inductive Bias for Distances: Neural Nets that Respect the Triangle InequalityCode1
Effective Reinforcement Learning through Evolutionary Surrogate-Assisted PrescriptionCode1
Hoplite: Efficient and Fault-Tolerant Collective Communication for Task-Based Distributed SystemsCode1
Reinforcement Learning Enhanced Quantum-inspired Algorithm for Combinatorial OptimizationCode1
Objective Mismatch in Model-based Reinforcement LearningCode1
SparseIDS: Learning Packet Sampling with Reinforcement LearningCode1
Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction StatesCode1
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Benchmark Results

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