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 791–800 of 15113 papers

TitleStatusHype
Zero-Shot Action Generalization with Limited Observations—0
HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agents—0
V-Max: A Reinforcement Learning Framework for Autonomous DrivingCode2
Regulatory DNA sequence Design with Reinforcement LearningCode1
MoRE: Unlocking Scalability in Reinforcement Learning for Quadruped Vision-Language-Action Models—0
Adaptive routing protocols for determining optimal paths in AI multi-agent systems: a priority- and learning-enhanced approach—0
Efficient Neural Clause-Selection Reinforcement—0
LMM-R1: Empowering 3B LMMs with Strong Reasoning Abilities Through Two-Stage Rule-Based RLCode4
Optimizing Test-Time Compute via Meta Reinforcement Fine-Tuning—0
VisRL: Intention-Driven Visual Perception via Reinforced ReasoningCode1
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Benchmark Results

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