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 2741–2750 of 15113 papers

TitleStatusHype
Stratified Expert Cloning with Adaptive Selection for User Retention in Large-Scale Recommender Systems—0
TW-CRL: Time-Weighted Contrastive Reward Learning for Efficient Inverse Reinforcement Learning—0
Algorithm Discovery With LLMs: Evolutionary Search Meets Reinforcement Learning—0
Physics-informed Modularized Neural Network for Advanced Building Control by Deep Reinforcement Learning—0
The Role of Environment Access in Agnostic Reinforcement Learning—0
Impact of Price Inflation on Algorithmic Collusion Through Reinforcement Learning Agents—0
OrbitZoo: Multi-Agent Reinforcement Learning Environment for Orbital Dynamics—0
Decision SpikeFormer: Spike-Driven Transformer for Decision Making—0
Learning Dual-Arm Coordination for Grasping Large Flat Objects—0
Improving Mixed-Criticality Scheduling with Reinforcement Learning—0
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Benchmark Results

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