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 2441–2450 of 15113 papers

TitleStatusHype
Bad-Policy Density: A Measure of Reinforcement Learning Hardness—0
AbFlowNet: Optimizing Antibody-Antigen Binding Energy via Diffusion-GFlowNet Fusion—0
BadGPT: Exploring Security Vulnerabilities of ChatGPT via Backdoor Attacks to InstructGPT—0
BADDr: Bayes-Adaptive Deep Dropout RL for POMDPs—0
A Multi-Agent Deep Reinforcement Learning Coordination Framework for Connected and Automated Vehicles at Merging Roadways—0
Contextual Policy Transfer in Reinforcement Learning Domains via Deep Mixtures-of-Experts—0
Contingency-Aware Exploration in Reinforcement Learning—0
A Multi-Agent Deep Reinforcement Learning Approach for a Distributed Energy Marketplace in Smart Grids—0
Backward Imitation and Forward Reinforcement Learning via Bi-directional Model Rollouts—0
Contextual Exploration Using a Linear Approximation Method Based on Satisficing—0
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Benchmark Results

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