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 11751–11800 of 15113 papers

TitleStatusHype
Uncertainty Quantification and Exploration for Reinforcement Learning—0
Influence-Based Multi-Agent ExplorationCode0
Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximation—0
Green Deep Reinforcement Learning for Radio Resource Management: Architecture, Algorithm Compression and Challenge—0
Network Randomization: A Simple Technique for Generalization in Deep Reinforcement LearningCode0
Modeling Cyber-Physical Human Systems via an Interplay Between Reinforcement Learning and Game Theory—0
Improving Gradient Estimation in Evolutionary Strategies With Past Descent Directions—0
Autonomous Driving using Safe Reinforcement Learning by Incorporating a Regret-based Human Lane-Changing Decision Model—0
Hierarchical Reinforcement Learning with Advantage-Based Auxiliary RewardsCode0
Agent with Warm Start and Active Termination for Plane Localization in 3D UltrasoundCode1
RLCard: A Toolkit for Reinforcement Learning in Card GamesCode0
Model-Based Reinforcement Learning Exploiting State-Action Equivalence—0
Black-box Optimizer with Implicit Natural Gradient—0
Model-based Reinforcement Learning for Predictions and Control for Limit Order Books—0
Multiple-objective Reinforcement Learning for Inverse Design and Identification—0
Improving Generalization in Meta Reinforcement Learning using Learned Objectives—0
Linear-Quadratic Mean-Field Reinforcement Learning: Convergence of Policy Gradient Methods—0
Ctrl-Z: Recovering from Instability in Reinforcement Learning—0
Fast Task-Adaptation for Tasks Labeled Using Natural Language in Reinforcement Learning—0
Defensive Escort Teams via Multi-Agent Deep Reinforcement Learning—0
Imagined Value Gradients: Model-Based Policy Optimization with Transferable Latent Dynamics Models—0
A Dual-Hormone Closed-Loop Delivery System for Type 1 Diabetes Using Deep Reinforcement Learning—0
Integrating Behavior Cloning and Reinforcement Learning for Improved Performance in Dense and Sparse Reward Environments—0
Tactical Reward Shaping: Bypassing Reinforcement Learning with Strategy-Based Goals—0
TorchBeast: A PyTorch Platform for Distributed RLCode0
Multi-Agent Reinforcement Learning for Order-dispatching via Order-Vehicle Distribution Matching—0
Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning?—0
Multi-step Greedy Reinforcement Learning Algorithms—0
Self-Paced Contextual Reinforcement LearningCode1
Reinforcement Learning with Structured Hierarchical Grammar Representations of Actions—0
Probabilistic Successor Representations with Kalman Temporal Differences—0
Biased Aggregation, Rollout, and Enhanced Policy Improvement for Reinforcement Learning—0
Attention-based Fault-tolerant Approach for Multi-agent Reinforcement Learning Systems—0
DeepMNavigate: Deep Reinforced Multi-Robot Navigation Unifying Local & Global Collision Avoidance—0
I'm sorry Dave, I'm afraid I can't do that, Deep Q-learning from forbidden action—0
Discounted Reinforcement Learning Is Not an Optimization Problem—0
Zero Shot Learning on Simulated Robots—0
Manufacturing Dispatching using Reinforcement and Transfer Learning—0
Using Logical Specifications of Objectives in Multi-Objective Reinforcement Learning—0
SensorDrop: A Reinforcement Learning Framework for Communication Overhead Reduction on the Edge—0
Machine learning strategies for path-planning microswimmers in turbulent flows—0
Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized CriticsCode0
Benchmarking Batch Deep Reinforcement Learning AlgorithmsCode1
Generalized Inner Loop Meta-LearningCode2
Hybrid Zero Dynamics Inspired Feedback Control Policy Design for 3D Bipedal Locomotion using Reinforcement Learning—0
Review of Learning-based Longitudinal Motion Planning for Autonomous Vehicles: Research Gaps between Self-driving and Traffic Congestion—0
Language is Power: Representing States Using Natural Language in Reinforcement Learning—0
QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement LearningCode0
Relationship Explainable Multi-objective Optimization Via Vector Value Function Based Reinforcement Learning—0
Never Worse, Mostly Better: Stable Policy Improvement in Deep Reinforcement Learning—0
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Benchmark Results

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