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 5026–5050 of 15113 papers

TitleStatusHype
Sampling Through the Lens of Sequential Decision Making—0
Path Planning of Cleaning Robot with Reinforcement Learning—0
Metric Residual Networks for Sample Efficient Goal-Conditioned Reinforcement LearningCode1
Data-driven End-to-end Learning of Pole Placement Control for Nonlinear Dynamics via Koopman Invariant Subspaces—0
A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement LearningCode2
A Deep Reinforcement Learning-based Adaptive Charging Policy for Wireless Rechargeable Sensor Networks—0
Hybrid UAV-enabled Secure Offloading via Deep Reinforcement Learning—0
Reinforcement Learning to Rank with Coarse-grained Labels—0
PD-MORL: Preference-Driven Multi-Objective Reinforcement Learning AlgorithmCode1
Making Reinforcement Learning Work on Swimmer—0
Reward Design For An Online Reinforcement Learning Algorithm Supporting Oral Self-CareCode0
Deep Reinforcement Learning for RIS-Assisted FD Systems: Single or Distributed RIS?—0
Transformer-based Value Function Decomposition for Cooperative Multi-agent Reinforcement Learning in StarCraftCode1
Online 3D Bin Packing Reinforcement Learning Solution with Buffer—0
Widely Used and Fast De Novo Drug Design by a Protein Sequence-Based Reinforcement Learning Model—0
Trustworthy Federated Learning via Blockchain—0
RLang: A Declarative Language for Describing Partial World Knowledge to Reinforcement Learning Agents—0
Low Emission Building Control with Zero-Shot Reinforcement Learning—0
Diffusion Policies as an Expressive Policy Class for Offline Reinforcement LearningCode2
Bayesian Soft Actor-Critic: A Directed Acyclic Strategy Graph Based Deep Reinforcement LearningCode1
Distributionally Robust Model-Based Offline Reinforcement Learning with Near-Optimal Sample Complexity—0
A Modular Framework for Reinforcement Learning Optimal ExecutionCode1
Multi-Agent Reinforcement Learning with Graph Convolutional Neural Networks for optimal Bidding Strategies of Generation Units in Electricity Markets—0
Towards Sequence-Level Training for Visual TrackingCode1
Mixed-Precision Neural Networks: A Survey—0
Show:102550
← PrevPage 202 of 605Next →

Benchmark Results

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