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 5051–5075 of 15113 papers

TitleStatusHype
Plug-and-Play Model-Agnostic Counterfactual Policy Synthesis for Deep Reinforcement Learning based Recommendation—0
Robust Reinforcement Learning using Offline DataCode1
Vehicle Type Specific Waypoint Generation—0
Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2—0
Multi-Task Fusion via Reinforcement Learning for Long-Term User Satisfaction in Recommender Systems—0
Object Detection with Deep Reinforcement LearningCode1
On the Importance of Critical Period in Multi-stage Reinforcement Learning—0
Automating DBSCAN via Deep Reinforcement LearningCode1
Exploring the trade off between human driving imitation and safety for traffic simulation—0
Generalized Reinforcement Learning: Experience Particles, Action Operator, Reinforcement Field, Memory Association, and Decision Concepts—0
Intrinsically Motivated Learning of Causal World Models—0
Basis for Intentions: Efficient Inverse Reinforcement Learning using Past ExperienceCode1
Attribute Controllable Beautiful Caucasian Face Generation by Aesthetics Driven Reinforcement Learning—0
From Scratch to Sketch: Deep Decoupled Hierarchical Reinforcement Learning for Robotic Sketching AgentCode1
Continual Reinforcement Learning with TELLA—0
Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgets—0
Optimal scheduling of entropy regulariser for continuous-time linear-quadratic reinforcement learning—0
A Game-Theoretic Perspective of Generalization in Reinforcement Learning—0
Multi-agent reinforcement learning for intent-based service assurance in cellular networks—0
Socially Intelligent Genetic Agents for the Emergence of Explicit NormsCode0
Compositional Reinforcement Learning for Discrete-Time Stochastic Control Systems—0
A Cooperation Graph Approach for Multiagent Sparse Reward Reinforcement LearningCode2
Human Decision Makings on Curriculum Reinforcement Learning with Difficulty Adjustment—0
Backward Imitation and Forward Reinforcement Learning via Bi-directional Model Rollouts—0
DL-DRL: A double-level deep reinforcement learning approach for large-scale task scheduling of multi-UAV—0
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Benchmark Results

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