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 4851–4875 of 15113 papers

TitleStatusHype
Reinforcement Learning for Feedback-Enabled Cyber Resilience—0
Reinforcement Learning for Finite-Horizon Restless Multi-Armed Multi-Action Bandits—0
Reinforcement Learning for Flexibility Design Problems—0
Reinforcement Learning for Game-Theoretic Resource Allocation on Graphs—0
On the (In)Tractability of Reinforcement Learning for LTL Objectives—0
Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges—0
Reinforcement Learning for Hanabi—0
Reinforcement Learning for Hardware Security: Opportunities, Developments, and Challenges—0
Reinforcement Learning for Heterogeneous Teams with PALO Bounds—0
Reinforcement Learning for Improving Object Detection—0
Reinforcement Learning for Individual Optimal Policy from Heterogeneous Data—0
Reinforcement Learning for Industrial Control Network Cyber Security Orchestration—0
Reinforcement learning for instance segmentation with high-level priors—0
Reinforcement Learning for Integer Programming: Learning to Cut—0
Reinforcement Learning for Intelligent Healthcare Systems: A Comprehensive Survey—0
Reinforcement Learning for IoT Security: A Comprehensive Survey—0
Reinforcement Learning for Joint V2I Network Selection and Autonomous Driving Policies—0
Reinforcement Learning for Jump-Diffusions, with Financial Applications—0
Reinforcement Learning for Learning of Dynamical Systems in Uncertain Environment: a Tutorial—0
Reinforcement Learning for Learning Rate Control—0
Reinforcement learning for linear-convex models with jumps via stability analysis of feedback controls—0
Reinforcement Learning for Linear Quadratic Control is Vulnerable Under Cost Manipulation—0
Reinforcement Learning for Load-balanced Parallel Particle Tracing—0
Reinforcement Learning for Location-Aware Scheduling—0
Reinforcement Learning for Long-Horizon Interactive LLM Agents—0
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Benchmark Results

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