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 6401–6450 of 15113 papers

TitleStatusHype
DL-DRL: A double-level deep reinforcement learning approach for large-scale task scheduling of multi-UAV—0
Backward Imitation and Forward Reinforcement Learning via Bi-directional Model Rollouts—0
Human Decision Makings on Curriculum Reinforcement Learning with Difficulty Adjustment—0
Towards Augmented Microscopy with Reinforcement Learning-Enhanced WorkflowsCode0
Transferable Multi-Agent Reinforcement Learning with Dynamic Participating Agents—0
Supervised and Reinforcement Learning from Observations in Reconnaissance Blind Chess—0
Reinforcement Learning for Joint V2I Network Selection and Autonomous Driving Policies—0
AACC: Asymmetric Actor-Critic in Contextual Reinforcement Learning—0
Deep VULMAN: A Deep Reinforcement Learning-Enabled Cyber Vulnerability Management Framework—0
A Lightweight Transmission Parameter Selection Scheme Using Reinforcement Learning for LoRaWAN—0
Joint Sensing and Communications for Deep Reinforcement Learning-based Beam Management in 6G—0
Chemotaxis of sea urchin sperm cells through deep reinforcement learning—0
Digital Twin-Assisted Efficient Reinforcement Learning for Edge Task Scheduling—0
Smart caching in a Data Lake for High Energy Physics analysis—0
Mitigating Off-Policy Bias in Actor-Critic Methods with One-Step Q-learning: A Novel Correction ApproachCode0
VacciNet: Towards a Smart Framework for Learning the Distribution Chain Optimization of Vaccines for a Pandemic—0
Retrieval of surgical phase transitions using reinforcement learning—0
Hierarchical Reinforcement Learning for Precise Soccer Shooting Skills using a Quadrupedal Robot—0
Learning to Grasp on the Moon from 3D Octree Observations with Deep Reinforcement Learning—0
A Maintenance Planning Framework using Online and Offline Deep Reinforcement Learning—0
Learning to generate Reliable Broadcast Algorithms—0
Robot Policy Learning from Demonstration Using Advantage Weighting and Early Termination—0
Using Chatbots to Teach Languages—0
Solving the vehicle routing problem with deep reinforcement learning—0
Reinforcement learning with experience replay and adaptation of action dispersion—0
A Bayesian Approach to Learning Bandit Structure in Markov Decision Processes—0
Deep Reinforcement Learning for System-on-Chip: Myths and Realities—0
Cyclic Policy Distillation: Sample-Efficient Sim-to-Real Reinforcement Learning with Domain RandomizationCode0
Combining Evolutionary Search with Behaviour Cloning for Procedurally Generated Content—0
Meta Reinforcement Learning with Successor Feature Based Context—0
Sample-efficient Safe Learning for Online Nonlinear Control with Control Barrier Functions—0
Sampling Attacks on Meta Reinforcement Learning: A Minimax Formulation and Complexity AnalysisCode0
Raising Student Completion Rates with Adaptive Curriculum and Contextual Bandits—0
Playing a 2D Game Indefinitely using NEAT and Reinforcement Learning—0
RangL: A Reinforcement Learning Competition Platform—0
Latent Properties of Lifelong Learning Systems—0
Graph Inverse Reinforcement Learning from Diverse Videos—0
Dynamic Shielding for Reinforcement Learning in Black-Box Environments—0
Distributional Actor-Critic Ensemble for Uncertainty-Aware Continuous Control—0
A Contact-Safe Reinforcement Learning Framework for Contact-Rich Robot Manipulation—0
POSET-RL: Phase ordering for Optimizing Size and Execution Time using Reinforcement Learning—0
Structural Similarity for Improved Transfer in Reinforcement Learning—0
Multi-Objective Provisioning of Network Slices using Deep Reinforcement Learning—0
Safe and Robust Experience Sharing for Deterministic Policy Gradient AlgorithmsCode0
Unsupervised Training for Neural TSP Solver—0
Semi-analytical Industrial Cooling System Model for Reinforcement Learning—0
Offline Reinforcement Learning at Multiple Frequencies—0
Planning and Learning: Path-Planning for Autonomous Vehicles, a Review of the Literature—0
Branch Ranking for Efficient Mixed-Integer Programming via Offline Ranking-based Policy Learning—0
Live in the Moment: Learning Dynamics Model Adapted to Evolving PolicyCode0
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Benchmark Results

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