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 6376–6400 of 15113 papers

TitleStatusHype
Online 3D Bin Packing Reinforcement Learning Solution with Buffer—0
Deep Reinforcement Learning for RIS-Assisted FD Systems: Single or Distributed RIS?—0
Widely Used and Fast De Novo Drug Design by a Protein Sequence-Based Reinforcement Learning Model—0
Trustworthy Federated Learning via Blockchain—0
RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents—0
Low Emission Building Control with Zero-Shot Reinforcement Learning—0
Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity—0
Multi-Agent Reinforcement Learning with Graph Convolutional Neural Networks for optimal Bidding Strategies of Generation Units in Electricity Markets—0
Mixed-Precision Neural Networks: A Survey—0
Plug-and-Play Model-Agnostic Counterfactual Policy Synthesis for Deep Reinforcement Learning based Recommendation—0
Intrinsically Motivated Learning of Causal World Models—0
Exploring the trade off between human driving imitation and safety for traffic simulation—0
Attribute Controllable Beautiful Caucasian Face Generation by Aesthetics Driven Reinforcement Learning—0
Generalized Reinforcement Learning: Experience Particles, Action Operator, Reinforcement Field, Memory Association, and Decision Concepts—0
Vehicle Type Specific Waypoint Generation—0
Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2—0
On the Importance of Critical Period in Multi-stage Reinforcement Learning—0
Multi-Task Fusion via Reinforcement Learning for Long-Term User Satisfaction in Recommender Systems—0
Optimal scheduling of entropy regulariser for continuous-time linear-quadratic reinforcement learning—0
Continual Reinforcement Learning with TELLA—0
Learning-Based Client Selection for Federated Learning Services Over Wireless Networks with Constrained Monetary Budgets—0
A Game-Theoretic Perspective of Generalization in Reinforcement Learning—0
Socially Intelligent Genetic Agents for the Emergence of Explicit NormsCode0
Multi-agent reinforcement learning for intent-based service assurance in cellular networks—0
Compositional Reinforcement Learning for Discrete-Time Stochastic Control Systems—0
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Benchmark Results

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