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 3551–3600 of 15113 papers

TitleStatusHype
AdaCoT: Pareto-Optimal Adaptive Chain-of-Thought Triggering via Reinforcement Learning—0
Designing Optimal Dynamic Treatment Regimes: A Causal Reinforcement Learning Approach—0
Deep-Reinforcement-Learning-Based Scheduling with Contiguous Resource Allocation for Next-Generation Cellular Systems—0
Deep Reinforcement Learning Based Semi-Autonomous Control for Robotic Surgery—0
Deep Reinforcement Learning-based Text Anonymization against Private-Attribute Inference—0
Deep Reinforcement Learning Boosted by External Knowledge—0
Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments—0
Deep Reinforcement Learning Controller for 3D Path-following and Collision Avoidance by Autonomous Underwater Vehicles—0
Counterfactual Explanation Policies in RL—0
Deep Reinforcement Learning Designed Shinnar-Le Roux RF Pulse using Root-Flipping: DeepRF_SLR—0
Deep Reinforcement Learning Discovers Internal Models—0
Deep reinforcement learning driven inspection and maintenance planning under incomplete information and constraints—0
A Strong Baseline for Batch Imitation Learning—0
Deep Reinforcement Learning (DRL): Another Perspective for Unsupervised Wireless Localization—0
Counterfactual Credit Assignment in Model-Free Reinforcement Learning—0
A physics-informed reinforcement learning approach for the interfacial area transport in two-phase flow—0
Agent based modelling for continuously varying supply chains—0
Deep Reinforcement Learning for Adaptive Traffic Signal Control—0
Deep Reinforcement Learning for Adaptive Learning Systems—0
Accelerating the Computation of UCB and Related Indices for Reinforcement Learning—0
Designing realistic RL environment for power systems—0
Deep Reinforcement Learning for Asset Allocation in US Equities—0
Deep Reinforcement Learning for Asset Allocation: Reward Clipping—0
Design of Restricted Normalizing Flow towards Arbitrary Stochastic Policy with Computational Efficiency—0
A Stochastic Composite Augmented Lagrangian Method For Reinforcement Learning—0
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