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 2751–2775 of 15113 papers

TitleStatusHype
CyberForce: A Federated Reinforcement Learning Framework for Malware Mitigation—0
d1: Scaling Reasoning in Diffusion Large Language Models via Reinforcement Learning—0
Curriculum Learning with a Progression Function—0
A Lifetime Extended Energy Management Strategy for Fuel Cell Hybrid Electric Vehicles via Self-Learning Fuzzy Reinforcement Learning—0
Curriculum Offline Imitating 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
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
Algorithmic Trading Using Continuous Action Space Deep Reinforcement Learning—0
Automated Driving with Evolution Capability: A Reinforcement Learning Method with Monotonic Performance Enhancement—0
Curriculum in Gradient-Based Meta-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
Curriculum Learning Based on Reward Sparseness for Deep Reinforcement Learning of Task Completion Dialogue Management—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
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Benchmark Results

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