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 8101–8125 of 15113 papers

TitleStatusHype
Navigation In Urban Environments Amongst Pedestrians Using Multi-Objective Deep Reinforcement Learning—0
REIN-2: Giving Birth to Prepared Reinforcement Learning Agents Using Reinforcement Learning Agents—0
Understanding the Safety Requirements for Learning-based Power Systems OperationsCode0
Scalable Traffic Signal Controls using Fog-Cloud Based Multiagent Reinforcement Learning—0
Reinforcement Learning for Systematic FX Trading—0
Reinforcement Learning In Two Player Zero Sum Simultaneous Action GamesCode0
Multi-condition multi-objective optimization using deep reinforcement learning—0
Hard instance learning for quantum adiabatic prime factorization—0
Braxlines: Fast and Interactive Toolkit for RL-driven Behavior Engineering beyond Reward Maximization—0
An In-depth Summary of Recent Artificial Intelligence Applications in Drug Design—0
An Augmented Reality Platform for Introducing Reinforcement Learning to K-12 Students with Robots—0
Deep Reinforcement Learning for Optimizing RIS-Assisted HD-FD Wireless Systems—0
Satisficing Paths and Independent Multi-Agent Reinforcement Learning in Stochastic Games—0
Human-Aware Robot Navigation via Reinforcement Learning with Hindsight Experience Replay and Curriculum Learning—0
Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning—0
Provably Efficient Black-Box Action Poisoning Attacks Against Reinforcement Learning—0
Theoretically Principled Deep RL Acceleration via Nearest Neighbor Function Approximation—0
Training Transition Policies via Distribution Matching for Complex TasksCode0
Revisiting Design Choices in Offline Model-Based Reinforcement Learning—0
Showing Your Offline Reinforcement Learning Work: Online Evaluation Budget Matters—0
CheerBots: Chatbots toward Empathy and Emotionusing Reinforcement Learning—0
Learning to Centralize Dual-Arm Assembly—0
A MultiModal Social Robot Toward Personalized Emotion Interaction—0
Generalization in Deep RL for TSP Problems via Equivariance and Local Search—0
Arachnophobia Exposure Therapy using Experience-driven Procedural Content Generation via Reinforcement Learning (EDPCGRL)Code0
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Benchmark Results

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