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

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Showing 11826–11850 of 15113 papers

TitleStatusHype
A Distributed Model-Free Algorithm for Multi-hop Ride-sharing using Deep Reinforcement Learning—0
DADI: Dynamic Discovery of Fair Information with Adversarial Reinforcement Learning—0
Deep Reinforcement Learning for Distributed Uncoordinated Cognitive Radios Resource Allocation—0
Adaptive Sampling Quasi-Newton Methods for Derivative-Free Stochastic Optimization—0
Deep reinforcement learning for market making in corporate bonds: beating the curse of dimensionality—0
Deep Decentralized Reinforcement Learning for Cooperative Control—0
Overcoming Catastrophic Interference in Online Reinforcement Learning with Dynamic Self-Organizing Maps—0
Navigation Agents for the Visually Impaired: A Sidewalk Simulator and ExperimentsCode0
Feedback Linearization for Unknown Systems via Reinforcement Learning—0
Constrained Reinforcement Learning Has Zero Duality Gap—0
Robust Model-free Reinforcement Learning with Multi-objective Bayesian Optimization—0
Biomimetic Ultra-Broadband Perfect Absorbers Optimised with Reinforcement Learning—0
Asynchronous Methods for Model-Based Reinforcement LearningCode0
Certified Adversarial Robustness for Deep Reinforcement Learning—0
Generalization in Reinforcement Learning with Selective Noise Injection and Information BottleneckCode0
Quantum enhancements for deep reinforcement learning in large spacesCode0
Entity Abstraction in Visual Model-Based Reinforcement LearningCode0
Model-Free Mean-Field Reinforcement Learning: Mean-Field MDP and Mean-Field Q-Learning—0
Neural Architecture Evolution in Deep Reinforcement Learning for Continuous Control—0
Minimax Weight and Q-Function Learning for Off-Policy Evaluation—0
Task-Oriented Language Grounding for Language Input with Multiple Sub-Goals of Non-Linear OrderCode0
BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement LearningCode0
Convergent Policy Optimization for Safe Reinforcement LearningCode0
Reinforcement Learning-Enabled Reliable Wireless Sensor Networks in Dynamic Underground Environments—0
ZPD Teaching Strategies for Deep Reinforcement Learning from DemonstrationsCode0
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Benchmark Results

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