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 9926–9950 of 15113 papers

TitleStatusHype
Driving-Policy Adaptive Safeguard for Autonomous Vehicles Using Reinforcement Learning—0
Are Gradient-based Saliency Maps Useful in Deep Reinforcement Learning?—0
BSODA: A Bipartite Scalable Framework for Online Disease Diagnosis—0
Combining Cognitive Modeling and Reinforcement Learning for Clarification in Dialogue—0
Is Long Horizon RL More Difficult Than Short Horizon RL?—0
ExpanRL: Hierarchical Reinforcement Learning for Course Concept Expansion in MOOCs—0
EcoLight: Intersection Control in Developing Regions Under Extreme Budget and Network Constraints—0
Improving Neural Machine Translation for Sanskrit-English—0
Improving the Naturalness and Diversity of Referring Expression Generation models using Minimum Risk Training—0
Almost Optimal Model-Free Reinforcement Learningvia Reference-Advantage Decomposition—0
Assessing and Accelerating Coverage in Deep Reinforcement Learning—0
Instance-based Generalization in Reinforcement Learning—0
Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning—0
Answer-driven Deep Question Generation based on Reinforcement Learning—0
A Local Temporal Difference Code for Distributional Reinforcement Learning—0
A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning—0
A new convergent variant of Q-learning with linear function approximation—0
Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory—0
Robust Multi-Agent Reinforcement Learning with Model Uncertainty—0
Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method—0
Security Analysis of Safe and Seldonian Reinforcement Learning Algorithms—0
R-learning in actor-critic model offers a biologically relevant mechanism for sequential decision-making—0
RL Unplugged: A Collection of Benchmarks for Offline Reinforcement LearningCode0
Text Simplification with Reinforcement Learning Using Supervised Rewards on Grammaticality, Meaning Preservation, and Simplicity—0
On Efficiency in Hierarchical Reinforcement Learning—0
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Benchmark Results

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