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 5751–5800 of 15113 papers

TitleStatusHype
Discrete Control in Real-World Driving Environments using Deep Reinforcement Learning—0
Distributed Energy Management and Demand Response in Smart Grids: A Multi-Agent Deep Reinforcement Learning Framework—0
Learning and Understanding a Disentangled Feature Representation for Hidden Parameters in Reinforcement Learning—0
Approximating Martingale Process for Variance Reduction in Deep Reinforcement Learning with Large State Space—0
Behavior Estimation from Multi-Source Data for Offline Reinforcement LearningCode0
Autotuning PID control using Actor-Critic Deep Reinforcement Learning—0
Offline Policy Evaluation and Optimization under Confounding—0
Offline Reinforcement Learning with Closed-Form Policy Improvement Operators—0
Multi-Agent Reinforcement Learning for Microprocessor Design Space Exploration—0
Symmetry Detection in Trajectory Data for More Meaningful Reinforcement Learning Representations—0
Offline Q-Learning on Diverse Multi-Task Data Both Scales And Generalizes—0
State-Aware Proximal Pessimistic Algorithms for Offline Reinforcement Learning—0
Tackling Visual Control via Multi-View Exploration Maximization—0
Beyond CAGE: Investigating Generalization of Learned Autonomous Network Defense Policies—0
Inapplicable Actions Learning for Knowledge Transfer in Reinforcement Learning—0
Causal Deep Reinforcement Learning Using Observational Data—0
Continuous Episodic Control—0
AcceRL: Policy Acceleration Framework for Deep Reinforcement Learning—0
Is Conditional Generative Modeling all you need for Decision-Making?—0
Learning from Good Trajectories in Offline Multi-Agent Reinforcement Learning—0
Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning—0
Hypernetworks for Zero-shot Transfer in Reinforcement Learning—0
Applying Deep Reinforcement Learning to the HP Model for Protein Structure PredictionCode0
Domain Generalization for Robust Model-Based Offline Reinforcement Learning—0
Combined Peak Reduction and Self-Consumption Using Proximal Policy Optimization—0
Computational Co-Design for Variable Geometry Truss—0
An Isolation-Aware Online Virtual Network Embedding via Deep Reinforcement Learning—0
Assistive Teaching of Motor Control Tasks to HumansCode0
Improving Proactive Dialog Agents Using Socially-Aware Reinforcement Learning—0
Pac-Man Pete: An extensible framework for building AI in VEX RoboticsCode0
Operator Splitting Value Iteration—0
SkillS: Adaptive Skill Sequencing for Efficient Temporally-Extended Exploration—0
Software Simulation and Visualization of Quantum Multi-Drone Reinforcement Learning—0
Explainable and Safe Reinforcement Learning for Autonomous Air MobilityCode0
Actively Learning Costly Reward Functions for Reinforcement LearningCode0
Introspection-based Explainable Reinforcement Learning in Episodic and Non-episodic Scenarios—0
Monte Carlo Tree Search Algorithms for Risk-Aware and Multi-Objective Reinforcement Learning—0
Reinforcement learning for traffic signal control in hybrid action space—0
Representation Learning for Continuous Action Spaces is Beneficial for Efficient Policy Learning—0
Reinforcement Learning Agent Design and Optimization with Bandwidth Allocation Model—0
Powderworld: A Platform for Understanding Generalization via Rich Task Distributions—0
On Instance-Dependent Bounds for Offline Reinforcement Learning with Linear Function Approximation—0
Prototypical context-aware dynamics generalization for high-dimensional model-based reinforcement learning—0
Safe Control and Learning Using the Generalized Action Governor—0
UNSAT Solver Synthesis via Monte Carlo Forest SearchCode0
The impact of moving expenses on social segregation: a simulation with RL and ABM—0
Greedy based Value Representation for Optimal Coordination in Multi-agent Reinforcement Learning—0
A Reinforcement Learning Badminton Environment for Simulating Player Tactics (Student Abstract)—0
A Reinforcement Learning Approach to Optimize Available Network Bandwidth Utilization—0
A Deep Reinforcement Learning Approach to Rare Event Estimation—0
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Benchmark Results

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