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 2771–2780 of 15113 papers

TitleStatusHype
Automated Design and Optimization of Distributed Filtering Circuits via Reinforcement Learning—0
Automated Database Indexing using Model-free Reinforcement Learning—0
Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction—0
Adaptive Discretization in Online Reinforcement Learning—0
Curriculum Offline Imitating Learning—0
Curriculum Reinforcement Learning via Morphology-Environment Co-Evolution—0
Cycle Consistency Driven Object Discovery—0
Automated Adversary Emulation for Cyber-Physical Systems via Reinforcement Learning—0
Algorithm Discovery With LLMs: Evolutionary Search Meets Reinforcement Learning—0
A Bandit Framework for Optimal Selection of Reinforcement Learning Agents—0
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Benchmark Results

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