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 6001–6025 of 15113 papers

TitleStatusHype
Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs—0
Semi-Markov Offline Reinforcement Learning for HealthcareCode0
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
A Survey of Multi-Agent Deep Reinforcement Learning with Communication—0
Backpropagation through Time and Space: Learning Numerical Methods with Multi-Agent Reinforcement Learning—0
How to Learn from Risk: Explicit Risk-Utility Reinforcement Learning for Efficient and Safe Driving Strategies—0
Coach-assisted Multi-Agent Reinforcement Learning Framework for Unexpected Crashed AgentsCode0
Latent-Variable Advantage-Weighted Policy Optimization for Offline RLCode1
Lazy-MDPs: Towards Interpretable Reinforcement Learning by Learning When to Act—0
CTDS: Centralized Teacher with Decentralized Student for Multi-Agent Reinforcement LearningCode1
PMIC: Improving Multi-Agent Reinforcement Learning with Progressive Mutual Information CollaborationCode1
Blocks Assemble! Learning to Assemble with Large-Scale Structured Reinforcement Learning—0
Multi-View Dreaming: Multi-View World Model with Contrastive Learning—0
Zipfian environments for Reinforcement LearningCode1
Non-Linear Reinforcement Learning in Large Action Spaces: Structural Conditions and Sample-efficiency of Posterior Sampling—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
L2Explorer: A Lifelong Reinforcement Learning Assessment EnvironmentCode0
Efficient Model-based Multi-agent Reinforcement Learning via Optimistic Equilibrium Computation—0
FRL-FI: Transient Fault Analysis for Federated Reinforcement Learning-Based Navigation Systems—0
The Multi-Agent Pickup and Delivery Problem: MAPF, MARL and Its Warehouse Applications—0
Switch Trajectory Transformer with Distributional Value Approximation for Multi-Task Reinforcement Learning—0
Uncertainty Estimation for Language Reward Models—0
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Benchmark Results

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