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 4201–4225 of 15113 papers

TitleStatusHype
Provable Risk-Sensitive Distributional Reinforcement Learning with General Function Approximation—0
Temporal Logic Specification-Conditioned Decision Transformer for Offline Safe Reinforcement Learning—0
reBandit: Random Effects based Online RL algorithm for Reducing Cannabis UseCode0
Learning to Program Variational Quantum Circuits with Fast Weights—0
C-GAIL: Stabilizing Generative Adversarial Imitation Learning with Control Theory—0
Monitoring Fidelity of Online Reinforcement Learning Algorithms in Clinical Trials—0
Reinforcement Learning Based Oscillation Dampening: Scaling up Single-Agent RL algorithms to a 100 AV highway field operational test—0
QF-tuner: Breaking Tradition in Reinforcement Learning—0
Concurrent Learning of Policy and Unknown Safety Constraints in Reinforcement Learning—0
Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function ApproximationCode0
AltGraph: Redesigning Quantum Circuits Using Generative Graph Models for Efficient Optimization—0
Trajectory-wise Iterative Reinforcement Learning Framework for Auto-bidding—0
PREDILECT: Preferences Delineated with Zero-Shot Language-based Reasoning in Reinforcement Learning—0
Reinforcement Learning with Elastic Time Steps—0
Automated Design and Optimization of Distributed Filtering Circuits via Reinforcement Learning—0
Improving a Proportional Integral Controller with Reinforcement Learning on a Throttle Valve Benchmark—0
AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning—0
Learning Dual-arm Object Rearrangement for Cartesian Robots—0
Dynamic Multi-Reward Weighting for Multi-Style Controllable GenerationCode0
Reinforcement learning-assisted quantum architecture search for variational quantum algorithms—0
MORE-3S:Multimodal-based Offline Reinforcement Learning with Shared Semantic SpacesCode0
Align Your Intents: Offline Imitation Learning via Optimal Transport—0
Antifragile Perimeter Control: Anticipating and Gaining from Disruptions with Reinforcement Learning—0
Deep Hedging with Market Impact—0
Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning—0
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Benchmark Results

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