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 5376–5400 of 15113 papers

TitleStatusHype
Deep reinforcement learning for fMRI prediction of Autism Spectrum Disorder—0
The State of Sparse Training in Deep Reinforcement LearningCode0
SMPL: Simulated Industrial Manufacturing and Process Control Learning EnvironmentsCode1
Logic-based Reward Shaping for Multi-Agent Reinforcement LearningCode0
Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement LearningCode2
SafeRL-Kit: Evaluating Efficient Reinforcement Learning Methods for Safe Autonomous Driving—0
Bootstrapped Transformer for Offline Reinforcement Learning—0
Fast Population-Based Reinforcement Learning on a Single MachineCode1
Generalised Policy Improvement with Geometric Policy Composition—0
Barrier Certified Safety Learning Control: When Sum-of-Square Programming Meets Reinforcement LearningCode1
A Look at Value-Based Decision-Time vs. Background Planning Methods Across Different Settings—0
Reinforcement Learning for Economic Policy: A New Frontier?—0
Reinforcement Learning-enhanced Shared-account Cross-domain Sequential RecommendationCode0
Double Check Your State Before Trusting It: Confidence-Aware Bidirectional Offline Model-Based ImaginationCode0
Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approaches—0
Contrastive Learning as Goal-Conditioned Reinforcement Learning—0
Autonomous Platoon Control with Integrated Deep Reinforcement Learning and Dynamic Programming—0
Automating the resolution of flight conflicts: Deep reinforcement learning in service of air traffic controllers—0
Mean-Semivariance Policy Optimization via Risk-Averse Reinforcement Learning—0
A Search-Based Testing Approach for Deep Reinforcement Learning AgentsCode1
Rethinking Reinforcement Learning for Recommendation: A Prompt Perspective—0
Training Discrete Deep Generative Models via Gapped Straight-Through EstimatorCode1
Revisiting Some Common Practices in Cooperative Multi-Agent Reinforcement Learning—0
Towards a Solution to Bongard Problems: A Causal Approach—0
Regularizing a Model-based Policy Stationary Distribution to Stabilize Offline Reinforcement LearningCode0
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Benchmark Results

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