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 481–490 of 15113 papers

TitleStatusHype
USPR: Learning a Unified Solver for Profiled RoutingCode0
Taming OOD Actions for Offline Reinforcement Learning: An Advantage-Based Approach—0
RL-DAUNCE: Reinforcement Learning-Driven Data Assimilation with Uncertainty-Aware Constrained Ensembles—0
Flow-GRPO: Training Flow Matching Models via Online RLCode7
Multi-agent Embodied AI: Advances and Future Directions—0
Enhancing Reinforcement Learning for the Floorplanning of Analog ICs with Beam Search—0
Extending a Quantum Reinforcement Learning Exploration Policy with Flags to Connect Four—0
Putting the Value Back in RL: Better Test-Time Scaling by Unifying LLM Reasoners With Verifiers—0
Large Language Models are Autonomous Cyber DefendersCode0
ZeroSearch: Incentivize the Search Capability of LLMs without SearchingCode5
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Benchmark Results

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