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 7551–7575 of 15113 papers

TitleStatusHype
Identifying optimal cycles in quantum thermal machines with reinforcement-learningCode0
Learning Meta Representations for Agents in Multi-Agent Reinforcement Learning—0
Integrated Decision and Control at Multi-Lane Intersections with Mixed Traffic Flow—0
SurRoL: An Open-source Reinforcement Learning Centered and dVRK Compatible Platform for Surgical Robot LearningCode1
Reinforcement Learning Based Sparse Black-box Adversarial Attack on Video Recognition Models—0
A Policy Efficient Reduction Approach to Convex Constrained Deep Reinforcement Learning—0
Influence-Based Reinforcement Learning for Intrinsically-Motivated Agents—0
Active Inference for Stochastic ControlCode1
Deep Reinforcement Learning for Wireless Resource Allocation Using Buffer State Information—0
Reinforcement Learning based Condition-oriented Maintenance Scheduling for Flow Line SystemsCode1
ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language ModelsCode1
Reinforcement Learning-powered Semantic Communication via Semantic SimilarityCode1
WAD: A Deep Reinforcement Learning Agent for Urban Autonomous Driving—0
Deep Reinforcement Learning for Dynamic Band Switch in Cellular-Connected UAV—0
Federated Reinforcement Learning: Techniques, Applications, and Open Challenges—0
Adaptive Control of Differentially Private Linear Quadratic Systems—0
Model-based Chance-Constrained Reinforcement Learning via Separated Proportional-Integral Lagrangian—0
Robust Model-based Reinforcement Learning for Autonomous Greenhouse Control—0
When should agents explore?—0
Responsive Regulation of Dynamic UAV Communication Networks Based on Deep Reinforcement LearningCode1
Adversary agent reinforcement learning for pursuit-evasion—0
Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey—0
Self-optimizing adaptive optics control with Reinforcement Learning for high-contrast imaging—0
Entropy-Aware Model Initialization for Effective Exploration in Deep Reinforcement Learning—0
Robust Risk-Aware Reinforcement LearningCode1
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Benchmark Results

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