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 2651–2660 of 15113 papers

TitleStatusHype
A Generalised and Adaptable Reinforcement Learning Stopping MethodCode0
Stabilizing Temporal Difference Learning via Implicit Stochastic Recursion—0
MULE: Multi-terrain and Unknown Load Adaptation for Effective Quadrupedal Locomotion—0
A General Approach of Automated Environment Design for Learning the Optimal Power Flow—0
Implicit Neural-Representation Learning for Elastic Deformable-Object Manipulations—0
Directly Forecasting Belief for Reinforcement Learning with DelaysCode0
Leveraging Partial SMILES Validation Scheme for Enhanced Drug Design in Reinforcement Learning Frameworks—0
SmallPlan: Leverage Small Language Models for Sequential Path Planning with Simulation-Powered, LLM-Guided DistillationCode0
Phi-4-Mini-Reasoning: Exploring the Limits of Small Reasoning Language Models in Math—0
Adaptive 3D UI Placement in Mixed Reality Using Deep Reinforcement Learning—0
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Benchmark Results

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