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 7426–7450 of 15113 papers

TitleStatusHype
Towards Multi-Agent Reinforcement Learning using Quantum Boltzmann Machines—0
MEPG: A Minimalist Ensemble Policy Gradient Framework for Deep Reinforcement Learning—0
Locality Matters: A Scalable Value Decomposition Approach for Cooperative Multi-Agent Reinforcement Learning—0
Long-Term Exploration in Persistent MDPsCode0
Generalization in Text-based Games via Hierarchical Reinforcement LearningCode0
Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks—0
A Distance-based Anomaly Detection Framework for Deep Reinforcement Learning—0
AutoPhoto: Aesthetic Photo Capture using Reinforcement LearningCode1
Learning offline: memory replay in biological and artificial reinforcement learning—0
Deep Policies for Online Bipartite Matching: A Reinforcement Learning ApproachCode1
A Reinforcement Learning Approach to the Stochastic Cutting Stock Problem—0
ACReL: Adversarial Conditional value-at-risk Reinforcement Learning—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
Learning Natural Language Generation from Scratch—0
A Survey of Text Games for Reinforcement Learning informed by Natural Language—0
Dual Behavior Regularized Reinforcement Learning—0
Greedy UnMixing for Q-Learning in Multi-Agent Reinforcement Learning—0
Regularize! Don't Mix: Multi-Agent Reinforcement Learning without Explicit Centralized Structures—0
Lifelong Robotic Reinforcement Learning by Retaining Experiences—0
Hindsight Foresight Relabeling for Meta-Reinforcement LearningCode0
Deep Reinforcement Learning Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications—0
Exploring the Training Robustness of Distributional Reinforcement Learning against Noisy State ObservationsCode0
POAR: Efficient Policy Optimization via Online Abstract State Representation Learning—0
Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation—0
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Benchmark Results

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