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 8501–8550 of 15113 papers

TitleStatusHype
The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication—0
Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation—0
CLAMGen: Closed-Loop Arm Motion Generation via Multi-view Vision-Based RL—0
Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target PredictionCode0
Discriminator Augmented Model-Based Reinforcement Learning—0
Hamiltonian Policy Optimization in Reinforcement Learning—0
An Exponential Lower Bound for Linearly-Realizable MDPs with Constant Suboptimality Gap—0
Learning 6DoF Grasping Using Reward-Consistent Demonstration—0
Assured Learning-enabled Autonomy: A Metacognitive Reinforcement Learning Framework—0
Meta-Adversarial Inverse Reinforcement Learning for Decision-making Tasks—0
Replacing Rewards with Examples: Example-Based Policy Search via Recursive ClassificationCode0
Unsupervised Contextual Paraphrase Generation using Lexical Control and Reinforcement Learning—0
Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement LearningCode0
Online Baum-Welch algorithm for Hierarchical Imitation LearningCode0
Variational quantum compiling with double Q-learning—0
Reinforcement Learning based on Scenario-tree MPC for ASVs—0
Provably Correct Optimization and Exploration with Non-linear PoliciesCode0
Regularized Softmax Deep Multi-Agent Q-Learning—0
Reinforcement Learning based on MPC/MHE for Unmodeled and Partially Observable Dynamics—0
Smart Scheduling based on Deep Reinforcement Learning for Cellular Networks—0
Improving Actor-Critic Reinforcement Learning via Hamiltonian Monte Carlo Method—0
IPAPRec: A promising tool for learning high-performance mapless navigation skills with deep reinforcement learning—0
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism—0
Bayesian Distributional Policy Gradients—0
Image Synthesis for Data Augmentation in Medical CT using Deep Reinforcement Learning—0
Human-Inspired Multi-Agent Navigation using Knowledge DistillationCode1
Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning—0
Integrated Decision and Control: Towards Interpretable and Computationally Efficient Driving IntelligenceCode1
Deep Reinforcement Learning-Aided RAN Slicing Enforcement for B5G Latency Sensitive Services—0
Maximum Entropy Reinforcement Learning with Mixture Policies—0
Weakly Supervised Reinforcement Learning for Autonomous Highway Driving via Virtual Safety CagesCode0
Near Optimal Policy Optimization via REPS—0
Decentralized Reinforcement Learning for Multi-Target Search and Detection by a Team of Drones—0
Infinite-Horizon Offline Reinforcement Learning with Linear Function Approximation: Curse of Dimensionality and Algorithm—0
A Practical Guide to Multi-Objective Reinforcement Learning and PlanningCode0
Hierarchical Reinforcement Learning Framework for Stochastic Spaceflight Campaign Design—0
Inclined Quadrotor Landing using Deep Reinforcement LearningCode1
Learning to Shape Rewards using a Game of Two Partners—0
Goal-constrained Sparse Reinforcement Learning for End-to-End Driving—0
Lyapunov Barrier Policy OptimizationCode1
Transfer Learning for Automated Test Case Prioritization Using XCSFCode0
Accelerating Online Reinforcement Learning via Model-Based Meta-Learning—0
Deep Reinforcement Learning for Band Selection in Hyperspectral Image ClassificationCode1
Autonomous Drone Racing with Deep Reinforcement Learning—0
Learning Symbolic Rules for Interpretable Deep Reinforcement Learning—0
Modelling Human Kinetics and Kinematics during Walking using Reinforcement Learning—0
Reinforcement Learning with Algorithms from Probabilistic Structure EstimationCode0
Sample-efficient Reinforcement Learning Representation Learning with Curiosity Contrastive Forward Dynamics ModelCode1
Simulation Studies on Deep Reinforcement Learning for Building Control with Human Interaction—0
Offline Reinforcement Learning with Fisher Divergence Critic Regularization—0
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Benchmark Results

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