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 14151–14200 of 15113 papers

TitleStatusHype
Markov Decision Processes with Continuous Side Information—0
Variational Adaptive-Newton Method for Explorative Learning—0
BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems—0
Finding Efficient Swimming Strategies in a Three Dimensional Chaotic Flow by Reinforcement Learning—0
Costate-focused models for reinforcement learning—0
Saliency-based Sequential Image Attention with Multiset Prediction—0
Loss Functions for Multiset Prediction—0
Reinforcement Learning in a large scale photonic Recurrent Neural Network—0
Classical Structured Prediction Losses for Sequence to Sequence Learning—0
SQLNet: Generating Structured Queries From Natural Language Without Reinforcement LearningCode2
Reinforcement Learning of Speech Recognition System Based on Policy Gradient and Hypothesis Selection—0
Towards the Use of Deep Reinforcement Learning with Global Policy For Query-based Extractive SummarisationCode0
Accelerated Method for Stochastic Composition Optimization with Nonsmooth Regularization—0
Applications of Deep Learning and Reinforcement Learning to Biological Data—0
An Empirical Analysis of Multiple-Turn Reasoning Strategies in Reading Comprehension Tasks—0
Worm-level Control through Search-based Reinforcement Learning—0
LatentPoison - Adversarial Attacks On The Latent SpaceCode0
Energy Storage Arbitrage in Real-Time Markets via Reinforcement Learning—0
Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games?Code0
Double Q(σ) and Q(σ, λ): Unifying Reinforcement Learning Control Algorithms—0
Composing Meta-Policies for Autonomous Driving Using Hierarchical Deep Reinforcement Learning—0
Policy Optimization by Genetic Distillation—0
Adaptive coordination of working-memory and reinforcement learning in non-human primates performing a trial-and-error problem solving taskCode0
A Unified Game-Theoretic Approach to Multiagent Reinforcement Learning—0
Automatic Text Summarization Using Reinforcement Learning with Embedding Features—0
Learning to Diagnose: Assimilating Clinical Narratives using Deep Reinforcement Learning—0
Intelligent Parameter Tuning in Optimization-based Iterative CT Reconstruction via Deep Reinforcement Learning—0
Acquiring Target Stacking Skills by Goal-Parameterized Deep Reinforcement Learning—0
Paraphrase Generation with Deep Reinforcement Learning—0
Regret Minimization for Partially Observable Deep Reinforcement LearningCode0
Backpropagation through the Void: Optimizing control variates for black-box gradient estimationCode0
TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement LearningCode0
Visualizing and Understanding Atari AgentsCode0
Automata-Guided Hierarchical Reinforcement Learning for Skill Composition—0
Learning Robust Rewards with Adversarial Inverse Reinforcement LearningCode1
Unifying Value Iteration, Advantage Learning, and Dynamic Policy Programming—0
Exponential improvements for quantum-accessible reinforcement learning—0
Predicting Head Movement in Panoramic Video: A Deep Reinforcement Learning ApproachCode0
Action-depedent Control Variates for Policy Optimization via Stein's IdentityCode0
Eigenoption Discovery through the Deep Successor RepresentationCode1
Artificial Intelligence as Structural Estimation: Economic Interpretations of Deep Blue, Bonanza, and AlphaGo—0
Sequence-to-Sequence ASR Optimization via Reinforcement Learning—0
Diff-DAC: Distributed Actor-Critic for Average Multitask Deep Reinforcement Learning—0
Distributional Reinforcement Learning with Quantile RegressionCode0
Inverse Reinforcement Learning Under Noisy Observations—0
Generalization Tower Network: A Novel Deep Neural Network Architecture for Multi-Task LearningCode0
Learning Approximate Stochastic Transition ModelsCode0
Accelerated Reinforcement Learning—0
Exploiting generalization in the subspaces for faster model-based learning—0
Insulin Regimen ML-based control for T2DM patients—0
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Benchmark Results

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