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 7076–7100 of 15113 papers

TitleStatusHype
Temporal Alignment for History Representation in Reinforcement LearningCode0
Standardized feature extraction from pairwise conflicts applied to the train rescheduling problem—0
On the Computational Consequences of Cost Function Design in Nonlinear Optimal Control—0
RL4ReAl: Reinforcement Learning for Register Allocation—0
Learning to Bid Long-Term: Multi-Agent Reinforcement Learning with Long-Term and Sparse Reward in Repeated Auction GamesCode0
Configuration Path Control—0
Automating Reinforcement Learning with Example-based ResetsCode0
Disentangling Abstraction from Statistical Pattern Matching in Human and Machine LearningCode0
Optimising Energy Efficiency in UAV-Assisted Networks using Deep Reinforcement Learning—0
Safe Controller for Output Feedback Linear Systems using Model-Based Reinforcement Learning—0
Reinforcement Learning Agents in Colonel BlottoCode0
Semi-Data-Aided Channel Estimation for MIMO Systems via Reinforcement Learning—0
Best-Response Bayesian Reinforcement Learning with Bayes-adaptive POMDPs for Centaurs—0
Enhancing Digital Health Services: A Machine Learning Approach to Personalized Exercise Goal Setting—0
Hybrid Transfer in Deep Reinforcement Learning for Ads Allocation—0
Learning List-wise Representation in Reinforcement Learning for Ads Allocation with Multiple Auxiliary Tasks—0
Safe Reinforcement Learning via Shielding under Partial Observability—0
Model-agnostic Counterfactual Synthesis Policy for Interactive Recommendation—0
A Reinforcement Learning Approach to Sensing Design in Resource-Constrained Wireless Networked Control SystemsCode0
Deep Page-Level Interest Network in Reinforcement Learning for Ads Allocation—0
Building Decision Forest via Deep Reinforcement Learning—0
Hysteresis-Based RL: Robustifying Reinforcement Learning-based Control Policies via Hybrid ControlCode0
Automating Staged Rollout with Reinforcement Learning—0
DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools—0
Maze Learning using a Hyperdimensional Predictive Processing Cognitive Architecture—0
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Benchmark Results

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