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 3181–3190 of 15113 papers

TitleStatusHype
CWAE-IRL: Formulating a supervised approach to Inverse Reinforcement Learning problem—0
CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation—0
Agent Probing Interaction Policies—0
A Survey on Reinforcement Learning Methods in Character Animation—0
A Survey on Reinforcement Learning Security with Application to Autonomous Driving—0
CycLight: learning traffic signal cooperation with a cycle-level strategy—0
CyGIL: A Cyber Gym for Training Autonomous Agents over Emulated Network Systems—0
A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems—0
d1: Scaling Reasoning in Diffusion Large Language Models via Reinforcement Learning—0
Credit-cognisant reinforcement learning for multi-agent cooperation—0
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Benchmark Results

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