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 9251–9300 of 15113 papers

TitleStatusHype
Improving Playtesting Coverage via Curiosity Driven Reinforcement Learning Agents—0
AoA-Based Pilot Assignment in Massive MIMO Systems Using Deep Reinforcement Learning—0
Reinforcement Learning for Deceiving Reactive Jammers in Wireless Networks—0
Self-Imitation Learning by Planning—0
Nearly Horizon-Free Offline Reinforcement Learning—0
Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning—0
The Gradient Convergence Bound of Federated Multi-Agent Reinforcement Learning with Efficient Communication—0
Discriminator Augmented Model-Based Reinforcement Learning—0
Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target PredictionCode0
Cautiously Optimistic Policy Optimization and Exploration with Linear Function Approximation—0
CLAMGen: Closed-Loop Arm Motion Generation via Multi-view Vision-Based RL—0
An Exponential Lower Bound for Linearly-Realizable MDPs with Constant Suboptimality Gap—0
Assured Learning-enabled Autonomy: A Metacognitive Reinforcement Learning Framework—0
Hamiltonian Policy Optimization in Reinforcement Learning—0
Learning 6DoF Grasping Using Reward-Consistent Demonstration—0
Meta-Adversarial Inverse Reinforcement Learning for Decision-making Tasks—0
Unsupervised Contextual Paraphrase Generation using Lexical Control and Reinforcement Learning—0
Replacing Rewards with Examples: Example-Based Policy Search via Recursive ClassificationCode0
Policy Information Capacity: Information-Theoretic Measure for Task Complexity in Deep Reinforcement LearningCode0
Reinforcement Learning based on MPC/MHE for Unmodeled and Partially Observable Dynamics—0
Smart Scheduling based on Deep Reinforcement Learning for Cellular Networks—0
Online Baum-Welch algorithm for Hierarchical Imitation LearningCode0
Variational quantum compiling with double Q-learning—0
Regularized Softmax Deep Multi-Agent Q-Learning—0
Reinforcement Learning based on Scenario-tree MPC for ASVs—0
Provably Correct Optimization and Exploration with Non-linear PoliciesCode0
Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism—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
Bayesian Distributional Policy Gradients—0
Deep Reinforcement Learning-Aided RAN Slicing Enforcement for B5G Latency Sensitive Services—0
Combining Pessimism with Optimism for Robust and Efficient Model-Based Deep Reinforcement Learning—0
Image Synthesis for Data Augmentation in Medical CT using Deep Reinforcement Learning—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
A Practical Guide to Multi-Objective Reinforcement Learning and PlanningCode0
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
Hierarchical Reinforcement Learning Framework for Stochastic Spaceflight Campaign Design—0
Learning to Shape Rewards using a Game of Two Partners—0
Goal-constrained Sparse Reinforcement Learning for End-to-End Driving—0
Reinforcement Learning with Algorithms from Probabilistic Structure EstimationCode0
Modelling Human Kinetics and Kinematics during Walking using Reinforcement Learning—0
Transfer Learning for Automated Test Case Prioritization Using XCSFCode0
Accelerating Online Reinforcement Learning via Model-Based Meta-Learning—0
Autonomous Drone Racing with Deep Reinforcement Learning—0
Learning Symbolic Rules for Interpretable Deep Reinforcement Learning—0
Investigating Value of Curriculum Reinforcement Learning in Autonomous Driving Under Diverse Road and Weather Conditions—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