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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 8376–8400 of 15113 papers

TitleStatusHype
A Multi-Agent Deep Reinforcement Learning Coordination Framework for Connected and Automated Vehicles at Merging Roadways—0
Dimension-Free Rates for Natural Policy Gradient in Multi-Agent Reinforcement Learning—0
Deep Reinforcement Learning-Based Long-Range Autonomous Valet Parking for Smart Cities—0
Hierarchies of Planning and Reinforcement Learning for Robot Navigation—0
Introducing Symmetries to Black Box Meta Reinforcement Learning—0
Benchmarking Lane-changing Decision-making for Deep Reinforcement Learning—0
Adversarial Training Blocks Generalization in Neural Policies—0
Estimation Error Correction in Deep Reinforcement Learning for Deterministic Actor-Critic MethodsCode0
Locality Matters: A Scalable Value Decomposition Approach for Cooperative Multi-Agent Reinforcement Learning—0
A Survey on Reinforcement Learning for Recommender Systems—0
Towards Multi-Agent Reinforcement Learning using Quantum Boltzmann Machines—0
Return Dispersion as an Estimator of Learning Potential for Prioritized Level Replay—0
MEPG: A Minimalist Ensemble Policy Gradient Framework for Deep Reinforcement Learning—0
Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks—0
A Distance-based Anomaly Detection Framework for Deep Reinforcement Learning—0
Long-Term Exploration in Persistent MDPsCode0
Learning offline: memory replay in biological and artificial reinforcement learning—0
Generalization in Text-based Games via Hierarchical Reinforcement LearningCode0
ACReL: Adversarial Conditional value-at-risk Reinforcement Learning—0
A Reinforcement Learning Approach to the Stochastic Cutting Stock Problem—0
A Survey of Text Games for Reinforcement Learning informed by Natural Language—0
Learning Natural Language Generation from Scratch—0
Reinforcement Learning for Finite-Horizon Restless Multi-Armed Multi-Action Bandits—0
Two Approaches to Building Collaborative, Task-Oriented Dialog Agents through Self-Play—0
Regularize! Don't Mix: Multi-Agent Reinforcement Learning without Explicit Centralized Structures—0
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Benchmark Results

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