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 7176–7200 of 15113 papers

TitleStatusHype
Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs—0
Meta-Reinforcement Learning for the Tuning of PI Controllers: An Offline Approach—0
Semi-Markov Offline Reinforcement Learning for HealthcareCode0
Strategic Maneuver and Disruption with Reinforcement Learning Approaches for Multi-Agent Coordination—0
A Survey of Multi-Agent Deep Reinforcement Learning with Communication—0
Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed AgentsCode0
Lazy-MDPs: Towards Interpretable Reinforcement Learning by Learning When to Act—0
How to Learn from Risk: Explicit Risk-Utility Reinforcement Learning for Efficient and Safe Driving Strategies—0
A Deep Reinforcement Learning-Based Caching Strategy for IoT Networks with Transient Data—0
COPA: Certifying Robust Policies for Offline Reinforcement Learning against Poisoning AttacksCode0
Backpropagation through Time and Space: Learning Numerical Methods with Multi-Agent Reinforcement Learning—0
Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning—0
Bi-Manual Manipulation and Attachment via Sim-to-Real Reinforcement Learning—0
A Differentiable Approach to Combinatorial Optimization using Dataless Neural Networks—0
An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility—0
Multi-View Dreaming: Multi-View World Model with Contrastive Learning—0
Non-Linear Reinforcement Learning in Large Action Spaces: Structural Conditions and Sample-efficiency of Posterior Sampling—0
Uncertainty Estimation for Language Reward Models—0
The Multi-Agent Pickup and Delivery Problem: MAPF, MARL and Its Warehouse Applications—0
Reinforcement Learning for Optimal Control of a District Cooling Energy Plant—0
Switch Trajectory Transformer with Distributional Value Approximation for Multi-Task Reinforcement Learning—0
Orchestrated Value Mapping for Reinforcement LearningCode0
FRL-FI: Transient Fault Analysis for Federated Reinforcement Learning-Based Navigation Systems—0
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation—0
L2Explorer: A Lifelong Reinforcement Learning Assessment EnvironmentCode0
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Benchmark Results

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