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 2381–2390 of 15113 papers

TitleStatusHype
AltGraph: Redesigning Quantum Circuits Using Generative Graph Models for Efficient Optimization—0
Foundation Policies with Hilbert RepresentationsCode2
HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent PathfindingCode1
Distributionally Robust Off-Dynamics Reinforcement Learning: Provable Efficiency with Linear Function ApproximationCode0
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
Trajectory-wise Iterative Reinforcement Learning Framework for Auto-bidding—0
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
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Benchmark Results

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