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 2831–2840 of 15113 papers

TitleStatusHype
A unified strategy for implementing curiosity and empowerment driven reinforcement learning—0
A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search—0
Adaptive Decision Making at the Intersection for Autonomous Vehicles Based on Skill Discovery—0
Data-Driven Robust Control Using Reinforcement Learning—0
Data-driven Under Frequency Load Shedding Using Reinforcement Learning—0
A Unified Off-Policy Evaluation Approach for General Value Function—0
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning—0
A Large Language Model-Driven Reward Design Framework via Dynamic Feedback for Reinforcement Learning—0
A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning—0
A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning—0
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Benchmark Results

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