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 5776–5800 of 15113 papers

TitleStatusHype
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