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 4826–4850 of 15113 papers

TitleStatusHype
Soft Action Priors: Towards Robust Policy Transfer—0
Locally Constrained Representations in Reinforcement Learning—0
Macro-Action-Based Multi-Agent/Robot Deep Reinforcement Learning under Partial Observability—0
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning—0
Graph Value Iteration—0
Deep Q-Network for AI Soccer—0
IRS Assisted NOMA Aided Mobile Edge Computing with Queue Stability: Heterogeneous Multi-Agent Reinforcement Learning—0
A Spiking Neural Network Learning Markov Chain—0
A Joint Imitation-Reinforcement Learning Framework for Reduced Baseline RegretCode0
A Deep Reinforcement Learning-Based Charging Scheduling Approach with Augmented Lagrangian for Electric Vehicle—0
Transferring Knowledge for Reinforcement Learning in Contact-Rich Manipulation—0
Meta-Reinforcement Learning for Adaptive Control of Second Order Systems—0
MAN: Multi-Action Networks LearningCode1
Safe reinforcement learning control for continuous-time nonlinear systems without a backup controller—0
Rewarding Episodic Visitation Discrepancy for Exploration in Reinforcement Learning—0
Measuring Interventional Robustness in Reinforcement LearningCode0
MSVIPER: Improved Policy Distillation for Reinforcement-Learning-Based Robot Navigation—0
BOME! Bilevel Optimization Made Easy: A Simple First-Order ApproachCode1
Age of Semantics in Cooperative Communications: To Expedite Simulation Towards Real via Offline Reinforcement Learning—0
Enforcing the consensus between Trajectory Optimization and Policy Learning for precise robot control—0
A Transferable and Automatic Tuning of Deep Reinforcement Learning for Cost Effective Phishing Detection—0
Active Predicting Coding: Brain-Inspired Reinforcement Learning for Sparse Reward Robotic Control Problems—0
Learning to Walk by Steering: Perceptive Quadrupedal Locomotion in Dynamic EnvironmentsCode1
"Guess what I'm doing": Extending legibility to sequential decision tasks—0
Latent Plans for Task-Agnostic Offline Reinforcement LearningCode1
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Benchmark Results

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