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 691–700 of 15113 papers

TitleStatusHype
TAR: Teacher-Aligned Representations via Contrastive Learning for Quadrupedal Locomotion—0
Synthesizing world models for bilevel planning—0
The Crucial Role of Problem Formulation in Real-World Reinforcement Learning—0
Harmonia: A Multi-Agent Reinforcement Learning Approach to Data Placement and Migration in Hybrid Storage Systems—0
Understanding R1-Zero-Like Training: A Critical PerspectiveCode5
Learning Adaptive Dexterous Grasping from Single Demonstrations—0
Unlocking Efficient Long-to-Short LLM Reasoning with Model MergingCode2
Offline Reinforcement Learning with Discrete Diffusion Skills—0
Generalized Phase Pressure Control Enhanced Reinforcement Learning for Traffic Signal ControlCode0
Model-Based Offline Reinforcement Learning with Adversarial Data Augmentation—0
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Benchmark Results

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