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
Interpretable Policies for Reinforcement Learning by Genetic Programming—0
A Low-Cost Ethics Shaping Approach for Designing Reinforcement Learning AgentsCode0
The Eigenoption-Critic Framework—0
MINOS: Multimodal Indoor Simulator for Navigation in Complex EnvironmentsCode0
Robust Deep Reinforcement Learning with Adversarial Attacks—0
Stochastic Answer Networks for Machine Reading ComprehensionCode0
Reinforced dynamics for enhanced sampling in large atomic and molecular systems—0
Deep Primal-Dual Reinforcement Learning: Accelerating Actor-Critic using Bellman Duality—0
End-to-End Offline Goal-Oriented Dialog Policy Learning via Policy Gradient—0
Noisy Natural Gradient as Variational InferenceCode0
A Deeper Look at Experience ReplayCode0
Interactive Reinforcement Learning for Object Grounding via Self-Talking—0
MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective IntelligenceCode0
Representation and Reinforcement Learning for Personalized Glycemic Control in Septic Patients—0
Progressive Neural Architecture SearchCode0
Online Reinforcement Learning in Stochastic Games—0
Optimistic posterior sampling for reinforcement learning: worst-case regret bounds—0
Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes—0
Natural Value Approximators: Learning when to Trust Past Estimates—0
Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs—0
Log-normality and Skewness of Estimated State/Action Values in Reinforcement Learning—0
Dynamic-Depth Context Tree Weighting—0
Adaptive Batch Size for Safe Policy Gradients—0
Compatible Reward Inverse Reinforcement Learning—0
Improved Learning in Evolution Strategies via Sparser Inter-Agent Network Topologies—0
Embodied Question AnsweringCode0
Comparing Deep Reinforcement Learning and Evolutionary Methods in Continuous Control—0
Transferring Autonomous Driving Knowledge on Simulated and Real Intersections—0
Safe Exploration for Identifying Linear Systems via Robust Optimization—0
Video Captioning via Hierarchical Reinforcement Learning—0
Reinforcement Learning To Adapt Speech Enhancement to Instantaneous Input Signal Quality—0
Deep Reinforcement Learning for De-Novo Drug DesignCode0
Can Complex Collective Behaviour Be Generated Through Randomness, Memory and a Pinch of Luck?—0
Automating Vehicles by Deep Reinforcement Learning using Task Separation with Hill Climbing—0
A Benchmarking Environment for Reinforcement Learning Based Task Oriented Dialogue Management—0
End-to-End Optimization of Task-Oriented Dialogue Model with Deep Reinforcement Learning—0
HoME: a Household Multimodal Environment—0
Crossmodal Attentive Skill LearnerCode0
Hierarchical Policy Search via Return-Weighted Density Estimation—0
Learning from Longitudinal Face Demonstration - Where Tractable Deep Modeling Meets Inverse Reinforcement Learning—0
A reinforcement learning algorithm for building collaboration in multi-agent systems—0
Risk-sensitive Inverse Reinforcement Learning via Semi- and Non-Parametric MethodsCode0
Deep Reinforcement Learning for Sepsis TreatmentCode0
Divide-and-Conquer Reinforcement LearningCode0
AI Safety GridworldsCode0
Generative Adversarial Network for Abstractive Text SummarizationCode0
Malaria Likelihood Prediction By Effectively Surveying Households Using Deep Reinforcement Learning—0
Ethical Challenges in Data-Driven Dialogue SystemsCode0
Cascade Attribute Learning Network—0
Asking the Difficult Questions: Goal-Oriented Visual Question Generation via Intermediate Rewards—0
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Benchmark Results

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