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 5101–5150 of 15113 papers

TitleStatusHype
A Bayesian Approach to Learning Bandit Structure in Markov Decision Processes—0
Unified Automatic Control of Vehicular Systems with Reinforcement LearningCode1
Solving the vehicle routing problem with deep reinforcement learning—0
Reinforcement learning with experience replay and adaptation of action dispersion—0
Sampling Attacks on Meta Reinforcement Learning: A Minimax Formulation and Complexity AnalysisCode0
Sample-efficient Safe Learning for Online Nonlinear Control with Control Barrier Functions—0
Meta Reinforcement Learning with Successor Feature Based Context—0
Combining Evolutionary Search with Behaviour Cloning for Procedurally Generated Content—0
Cyclic Policy Distillation: Sample-Efficient Sim-to-Real Reinforcement Learning with Domain RandomizationCode0
Deep Reinforcement Learning for System-on-Chip: Myths and Realities—0
Contrastive UCB: Provably Efficient Contrastive Self-Supervised Learning in Online Reinforcement LearningCode1
Graph Inverse Reinforcement Learning from Diverse Videos—0
Latent Properties of Lifelong Learning Systems—0
RangL: A Reinforcement Learning Competition Platform—0
Playing a 2D Game Indefinitely using NEAT and Reinforcement Learning—0
Raising Student Completion Rates with Adaptive Curriculum and Contextual Bandits—0
POSET-RL: Phase ordering for Optimizing Size and Execution Time using Reinforcement Learning—0
Multi-Objective Provisioning of Network Slices using Deep Reinforcement Learning—0
Structural Similarity for Improved Transfer in Reinforcement Learning—0
Distributional Actor-Critic Ensemble for Uncertainty-Aware Continuous Control—0
Dynamic Shielding for Reinforcement Learning in Black-Box Environments—0
A Contact-Safe Reinforcement Learning Framework for Contact-Rich Robot Manipulation—0
Safe and Robust Experience Sharing for Deterministic Policy Gradient AlgorithmsCode0
Unsupervised Training for Neural TSP Solver—0
Branch Ranking for Efficient Mixed-Integer Programming via Offline Ranking-based Policy Learning—0
Learning Bipedal Walking On Planned Footsteps For Humanoid RobotsCode3
Offline Reinforcement Learning at Multiple Frequencies—0
Semi-analytical Industrial Cooling System Model for Reinforcement Learning—0
Planning and Learning: Path-Planning for Autonomous Vehicles, a Review of the Literature—0
Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning—0
Cooperative Actor-Critic via TD Error Aggregation—0
Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks—0
Live in the Moment: Learning Dynamics Model Adapted to Evolving PolicyCode0
Online Reinforcement Learning for Periodic MDP—0
Adaptive Asynchronous Control Using Meta-learned Neural Ordinary Differential Equations—0
Post-processing Networks: Method for Optimizing Pipeline Task-oriented Dialogue Systems using Reinforcement LearningCode0
REPNP: Plug-and-Play with Deep Reinforcement Learning Prior for Robust Image Restoration—0
Lifelong Machine Learning of Functionally Compositional StructuresCode1
Learning Soccer Juggling Skills with Layer-wise Mixture-of-ExpertsCode1
Adaptive Decision Making at the Intersection for Autonomous Vehicles Based on Skill Discovery—0
Anti-Overestimation Dialogue Policy Learning for Task-Completion Dialogue System—0
Driver Dojo: A Benchmark for Generalizable Reinforcement Learning for Autonomous DrivingCode1
Halftoning with Multi-Agent Deep Reinforcement Learning—0
Epersist: A Self Balancing Robot Using PID Controller And Deep Reinforcement Learning—0
Hierarchical Kickstarting for Skill Transfer in Reinforcement LearningCode1
Learn Continuously, Act Discretely: Hybrid Action-Space Reinforcement Learning For Optimal Execution—0
Robust Knowledge Adaptation for Dynamic Graph Neural NetworksCode1
Towards Robust On-Ramp Merging via Augmented Multimodal Reinforcement Learning—0
Solving the optimal stopping problem with reinforcement learning: an application in financial option exerciseCode0
Strategising template-guided needle placement for MR-targeted prostate biopsy—0
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Benchmark Results

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