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 631–640 of 15113 papers

TitleStatusHype
Concise Reasoning via Reinforcement LearningCode1
Physics-informed Modularized Neural Network for Advanced Building Control by Deep Reinforcement Learning—0
Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement LearningCode1
OrbitZoo: Multi-Agent Reinforcement Learning Environment for Orbital Dynamics—0
Impact of Price Inflation on Algorithmic Collusion Through Reinforcement Learning Agents—0
Decision SpikeFormer: Spike-Driven Transformer for Decision Making—0
Improving Mixed-Criticality Scheduling with Reinforcement Learning—0
Offline and Distributional Reinforcement Learning for Wireless Communications—0
Algorithmic Prompt Generation for Diverse Human-like Teaming and Communication with Large Language Models—0
Enhanced Penalty-based Bidirectional Reinforcement Learning Algorithms—0
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Benchmark Results

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