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 2751–2800 of 15113 papers

TitleStatusHype
Data-driven End-to-end Learning of Pole Placement Control for Nonlinear Dynamics via Koopman Invariant Subspaces—0
Data-Assimilated Model-Based Reinforcement Learning for Partially Observed Chaotic Flows—0
A Lifetime Extended Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles via Self-Learning Fuzzy Reinforcement Learning—0
Data-assimilated model-informed reinforcement learning—0
Algorithms in Multi-Agent Systems: A Holistic Perspective from Reinforcement Learning and Game Theory—0
Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning—0
Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach—0
Data Augmentation for Continual RL via Adversarial Gradient Episodic Memory—0
Automated Lane Change Decision Making using Deep Reinforcement Learning in Dynamic and Uncertain Highway Environment—0
Algorithms for Learning Markov Field Policies—0
Adaptive Energy Management for Real Driving Conditions via Transfer Reinforcement Learning—0
Automated Hybrid Reward Scheduling via Large Language Models for Robotic Skill Learning—0
Automated Gain Control Through Deep Reinforcement Learning for Downstream Radar Object Detection—0
Algorithms for Batch Hierarchical Reinforcement Learning—0
Achieving Tighter Finite-Time Rates for Heterogeneous Federated Stochastic Approximation under Markovian Sampling—0
Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation—0
Algorithmic Trading Using Continuous Action Space Deep Reinforcement Learning—0
Automated Driving with Evolution Capability: A Reinforcement Learning Method with Monotonic Performance Enhancement—0
DARA: Dynamics-Aware Reward Augmentation in Offline Reinforcement Learning—0
Algorithmic Prompt Generation for Diverse Human-like Teaming and Communication with Large Language Models—0
Automated Discovery of Functional Actual Causes in Complex Environments—0
Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning—0
Automated Design and Optimization of Distributed Filtering Circuits via Reinforcement Learning—0
Automated Database Indexing using Model-free Reinforcement Learning—0
Algorithmic Improvements for Deep Reinforcement Learning applied to Interactive Fiction—0
Adaptive Discretization in Online Reinforcement Learning—0
DAQN: Deep Auto-encoder and Q-Network—0
Data Center Cooling System Optimization Using Offline Reinforcement Learning—0
Automated Adversary Emulation for Cyber-Physical Systems via Reinforcement Learning—0
Algorithm Discovery With LLMs: Evolutionary Search Meets Reinforcement Learning—0
A Bandit Framework for Optimal Selection of Reinforcement Learning Agents—0
Automata Guided Skill Composition—0
Automata Guided Reinforcement Learning With Demonstrations—0
AlgoPilot: Fully Autonomous Program Synthesis Without Human-Written Programs—0
Human-Robot Collaboration via Deep Reinforcement Learning of Real-World Interactions—0
AUTOMATA GUIDED HIERARCHICAL REINFORCEMENT LEARNING FOR ZERO-SHOT SKILL COMPOSITION—0
Automata-Guided Hierarchical Reinforcement Learning for Skill Composition—0
AlgaeDICE: Policy Gradient from Arbitrary Experience—0
Auto-MAP: A DQN Framework for Exploring Distributed Execution Plans for DNN Workloads—0
AutoHAS: Efficient Hyperparameter and Architecture Search—0
Achieving Real-Time LiDAR 3D Object Detection on a Mobile Device—0
Dantzig Selector with an Approximately Optimal Denoising Matrix and its Application to Reinforcement Learning—0
Auto Graph Encoder-Decoder for Neural Network Pruning—0
Policy Zooming: Adaptive Discretization-based Infinite-Horizon Average-Reward Reinforcement Learning—0
DADI: Dynamic Discovery of Fair Information with Adversarial Reinforcement Learning—0
Uniform Last-Iterate Guarantee for Bandits and Reinforcement Learning—0
A Learning Framework for High Precision Industrial Assembly—0
DACOM: Learning Delay-Aware Communication for Multi-Agent Reinforcement Learning—0
Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization—0
Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation—0
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Benchmark Results

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