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 2376–2400 of 15113 papers

TitleStatusHype
C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theory—0
Feedback Efficient Online Fine-Tuning of Diffusion ModelsCode2
GenNBV: Generalizable Next-Best-View Policy for Active 3D ReconstructionCode2
How Can LLM Guide RL? A Value-Based ApproachCode1
Concurrent Learning of Policy and Unknown Safety Constraints in Reinforcement Learning—0
AltGraph: Redesigning Quantum Circuits Using Generative Graph Models for Efficient Optimization—0
HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent PathfindingCode1
Foundation Policies with Hilbert RepresentationsCode2
Trajectory-wise Iterative Reinforcement Learning Framework for Auto-bidding—0
EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender SystemsCode2
PREDILECT: Preferences Delineated with Zero-Shot Language-based Reasoning in Reinforcement Learning—0
Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function ApproximationCode0
Reinforcement Learning with Elastic Time Steps—0
Automated Design and Optimization of Distributed Filtering Circuits via Reinforcement Learning—0
Dynamic Multi-Reward Weighting for Multi-Style Controllable GenerationCode0
Learning Dual-arm Object Rearrangement for Cartesian Robots—0
Improving a Proportional Integral Controller with Reinforcement Learning on a Throttle Valve Benchmark—0
Reinforcement learning-assisted quantum architecture search for variational quantum algorithms—0
AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning—0
Distinctive Image Captioning: Leveraging Ground Truth Captions in CLIP Guided Reinforcement LearningCode1
Deep Hedging with Market Impact—0
Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning—0
Reflect-RL: Two-Player Online RL Fine-Tuning for LMsCode1
Antifragile Perimeter Control: Anticipating and Gaining from Disruptions with Reinforcement Learning—0
XRL-Bench: A Benchmark for Evaluating and Comparing Explainable Reinforcement Learning TechniquesCode1
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Benchmark Results

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