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 4561–4570 of 15113 papers

TitleStatusHype
A model of discrete choice based on reinforcement learning under short-term memory—0
A Model Selection Approach for Corruption Robust Reinforcement Learning—0
A Modified Q-Learning Algorithm for Rate-Profiling of Polarization Adjusted Convolutional (PAC) Codes—0
A Modular and Transferable Reinforcement Learning Framework for the Fleet Rebalancing Problem—0
MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management—0
A Modular Test Bed for Reinforcement Learning Incorporation into Industrial Applications—0
AMRL: Aggregated Memory For Reinforcement Learning—0
A Multiagent CyberBattleSim for RL Cyber Operation Agents—0
A Multi-Agent Deep Reinforcement Learning Approach for a Distributed Energy Marketplace in Smart Grids—0
A Multi-Agent Deep Reinforcement Learning Coordination Framework for Connected and Automated Vehicles at Merging Roadways—0
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Benchmark Results

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