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 9801–9850 of 15113 papers

TitleStatusHype
Stability-Certified Reinforcement Learning via Spectral Normalization—0
Achieving Real-Time LiDAR 3D Object Detection on a Mobile Device—0
Deep Reinforcement Learning for Long-Short Portfolio OptimizationCode0
Towards Continual Reinforcement Learning: A Review and Perspectives—0
SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II—0
Unsupervised deep clustering and reinforcement learning can accurately segment MRI brain tumors with very small training sets—0
SPOTTER: Extending Symbolic Planning Operators through Targeted Reinforcement Learning—0
A State Representation Dueling Network for Deep Reinforcement Learning—0
Learning Vehicle Routing Problems using Policy Optimisation—0
Auto-Agent-Distiller: Towards Efficient Deep Reinforcement Learning Agents via Neural Architecture Search—0
Assured RL: Reinforcement Learning with Almost Sure Constraints—0
Deep Stock Trading: A Hierarchical Reinforcement Learning Framework for Portfolio Optimization and Order Execution—0
Intelligent Reflecting Surface Assisted Anti-Jamming Communications Based on Reinforcement Learning—0
Rethink AI-based Power Grid Control: Diving Into Algorithm Design—0
Scalable Deep Reinforcement Learning for Routing and Spectrum Access in Physical Layer—0
Self-Imitation Advantage Learning—0
QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement LearningCode0
Intelligent Resource Allocation in Dense LoRa Networks using Deep Reinforcement Learning—0
A Dynamic Penalty Function Approach for Constraints-Handling in Reinforcement Learning—0
Difference Rewards Policy Gradients—0
Explicitly Encouraging Low Fractional Dimensional Trajectories Via Reinforcement LearningCode0
myGym: Modular Toolkit for Visuomotor Robotic Tasks—0
Mobile Robot Planner with Low-cost Cameras Using Deep Reinforcement Learning—0
Reinforcement Learning-based Product Delivery Frequency Control—0
Model-Based Actor-Critic with Chance Constraint for Stochastic System—0
Quantum reinforcement learning in continuous action space—0
Minimax Strikes Back—0
Reinforcement Learning for Test Case Prioritization—0
Exact Reduction of Huge Action Spaces in General Reinforcement Learning—0
Hierarchical principles of embodied reinforcement learning: A review—0
Exploring Fluent Query Reformulations with Text-to-Text Transformers and Reinforcement Learning—0
Improving the Efficient Neural Architecture Search via Rewarding ModificationsCode0
Autotelic Agents with Intrinsically Motivated Goal-Conditioned Reinforcement Learning: a Short Survey—0
Interactive Question Clarification in Dialogue via Reinforcement Learning—0
Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning—0
Towards Optimal District Heating Temperature Control in China with Deep Reinforcement Learning—0
ViNG: Learning Open-World Navigation with Visual Goals—0
MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning—0
Model-free and Bayesian Ensembling Model-based Deep Reinforcement Learning for Particle Accelerator Control Demonstrated on the FERMI FELCode0
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation—0
A comparative evaluation of machine learning methods for robot navigation through human crowds—0
CARLA Real Traffic Scenarios -- novel training ground and benchmark for autonomous driving—0
Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data—0
Batch-Constrained Distributional Reinforcement Learning for Session-based Recommendation—0
Grounding Artificial Intelligence in the Origins of Human Behavior—0
Gegelati: Lightweight Artificial Intelligence through Generic and Evolvable Tangled Program Graphs—0
Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes—0
Super Reinforcement Bros: Playing Super Mario Bros with Reinforcement LearningCode0
Train a snake with reinforcement learning algorithms—0
Learning for MPC with Stability & Safety Guarantees—0
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Benchmark Results

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