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 3541–3550 of 15113 papers

TitleStatusHype
Reinforcement Learning as an Improvement Heuristic for Real-World Production Scheduling—0
IMRL: Integrating Visual, Physical, Temporal, and Geometric Representations for Enhanced Food Acquisition—0
Data-Efficient Quadratic Q-Learning Using LMIs—0
An Enhanced-State Reinforcement Learning Algorithm for Multi-Task Fusion in Large-Scale Recommender Systems—0
On-policy Actor-Critic Reinforcement Learning for Multi-UAV Exploration—0
A Reinforcement Learning Environment for Automatic Code Optimization in the MLIR Compiler—0
Robust Reinforcement Learning with Dynamic Distortion Risk MeasuresCode0
Instigating Cooperation among LLM Agents Using Adaptive Information Modulation—0
Mitigating Partial Observability in Adaptive Traffic Signal Control with Transformers—0
Offline Reinforcement Learning for Learning to Dispatch for Job Shop SchedulingCode0
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Benchmark Results

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