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 7901–7925 of 15113 papers

TitleStatusHype
FinRL: Deep Reinforcement Learning Framework to Automate Trading in Quantitative Finance—0
Optimization of the Model Predictive Control Meta-Parameters Through Reinforcement Learning—0
A Deep Reinforcement Learning Approach for Composing Moving IoT Services—0
d3rlpy: An Offline Deep Reinforcement Learning LibraryCode0
Development of collective behavior in newborn artificial agents—0
Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning—0
AI-based Radio Resource Management and Trajectory Design for PD-NOMA Communication in IRS-UAV Assisted Networks—0
Learning to Cooperate with Unseen Agent via Meta-Reinforcement Learning—0
Improving RNA Secondary Structure Design using Deep Reinforcement Learning—0
An Algorithmic Theory of Metacognition in Minds and Machines—0
Perturbational Complexity by Distribution Mismatch: A Systematic Analysis of Reinforcement Learning in Reproducing Kernel Hilbert Space—0
Supervised Advantage Actor-Critic for Recommender Systems—0
Successor Feature Neural Episodic Control—0
Model-Free Risk-Sensitive Reinforcement Learning—0
Towards Learning to Speak and Hear Through Multi-Agent Communication over a Continuous Acoustic Channel—0
Value Function Spaces: Skill-Centric State Abstractions for Long-Horizon Reasoning—0
Control of a fly-mimicking flyer in complex flow using deep reinforcement learning—0
Generalization in Dexterous Manipulation via Geometry-Aware Multi-Task Learning—0
Causal versus Marginal Shapley Values for Robotic Lever Manipulation Controlled using Deep Reinforcement Learning—0
Attacking Deep Reinforcement Learning-Based Traffic Signal Control Systems with Colluding Vehicles—0
Infinite Time Horizon Safety of Bayesian Neural NetworksCode0
Imagine Networks—0
Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies—0
Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-aware Dual-Agent Framework—0
Autonomous Attack Mitigation for Industrial Control Systems—0
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Benchmark Results

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