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

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Showing 3576–3600 of 15113 papers

TitleStatusHype
Count-Based Temperature Scheduling for Maximum Entropy Reinforcement Learning—0
Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization—0
Deep Reinforcement Learning for Autonomous Driving: A Survey—0
Deep Reinforcement Learning for Backup Strategies against Adversaries—0
Agent-Aware Dropout DQN for Safe and Efficient On-line Dialogue Policy Learning—0
Deep Reinforcement Learning for Chatbots Using Clustered Actions and Human-Likeness Rewards—0
Almost Optimal Model-Free Reinforcement Learning via Reference-Advantage Decomposition—0
A statistical learning strategy for closed-loop control of fluid flows—0
Deep Reinforcement Learning for Closed-Loop Blood Glucose Control—0
Deep Reinforcement Learning for Collaborative Edge Computing in Vehicular Networks—0
Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problems—0
A Cubic-regularized Policy Newton Algorithm for Reinforcement Learning—0
Deep reinforcement learning for complex evaluation of one-loop diagrams in quantum field theory—0
Deep Reinforcement Learning for Complex Manipulation Tasks with Sparse Feedback—0
Designing Composites with Target Effective Young's Modulus using Reinforcement Learning—0
Cost-Sensitive Portfolio Selection via Deep Reinforcement Learning—0
Deep Reinforcement Learning for Constrained Field Development Optimization in Subsurface Two-phase Flow—0
Deep Reinforcement Learning for Contact-Rich Skills Using Compliant Movement Primitives—0
Deep Reinforcement Learning for Continuous Docking Control of Autonomous Underwater Vehicles: A Benchmarking Study—0
Automatic Representation for Lifetime Value Recommender Systems—0
Cost-Sensitive Exploration in Bayesian Reinforcement Learning—0
Deep Reinforcement Learning for Cyber Security—0
Automatic Risk Adaptation in Distributional Reinforcement Learning—0
A State Representation for Diminishing Rewards—0
CostNet: An End-to-End Framework for Goal-Directed Reinforcement Learning—0
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Benchmark Results

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