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 521–530 of 15113 papers

TitleStatusHype
Adaptive 3D UI Placement in Mixed Reality Using Deep Reinforcement Learning—0
A Survey on GUI Agents with Foundation Models Enhanced by Reinforcement Learning—0
PRISM: Projection-based Reward Integration for Scene-Aware Real-to-Sim-to-Real Transfer with Few Demonstrations—0
Token-Efficient RL for LLM Reasoning—0
Reinforcement Learning-Based Heterogeneous Multi-Task Optimization in Semantic Broadcast Communications—0
AI Recommendation Systems for Lane-Changing Using Adherence-Aware Reinforcement Learning—0
Rulebook: bringing co-routines to reinforcement learning environmentsCode2
Interactive Double Deep Q-network: Integrating Human Interventions and Evaluative Predictions in Reinforcement Learning of Autonomous Driving—0
An Automated Reinforcement Learning Reward Design Framework with Large Language Model for Cooperative Platoon Coordination—0
LLMs for Engineering: Teaching Models to Design High Powered Rockets—0
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Benchmark Results

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