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 14501–14550 of 15113 papers

TitleStatusHype
Exploration--Exploitation in MDPs with Options—0
Cohesion-based Online Actor-Critic Reinforcement Learning for mHealth Intervention—0
Unsupervised Basis Function Adaptation for Reinforcement Learning—0
Fake News Mitigation via Point Process Based Intervention—0
Faster Reinforcement Learning Using Active SimulatorsCode0
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement LearningCode0
Deep Exploration via Randomized Value Functions—0
Investigation of Language Understanding Impact for Reinforcement Learning Based Dialogue Systems—0
Pseudorehearsal in value function approximation—0
Black-Box Data-efficient Policy Search for RoboticsCode0
Learning Cooperative Visual Dialog Agents with Deep Reinforcement LearningCode1
Multi-Timescale, Gradient Descent, Temporal Difference Learning with Linear Options—0
Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing—0
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability—0
Particle Value Functions—0
Using Reinforcement Learning for Demand Response of Domestic Hot Water Buffers: a Real-Life Demonstration—0
Minimax Regret Bounds for Reinforcement LearningCode0
Finite Sample Analysis of Two-Timescale Stochastic Approximation with Applications to Reinforcement Learning—0
Sensor Fusion for Robot Control through Deep Reinforcement Learning—0
Reinforcement Learning for Transition-Based Mention Detection—0
A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning—0
Micro-Objective Learning : Accelerating Deep Reinforcement Learning through the Discovery of Continuous Subgoals—0
Communications that Emerge through Reinforcement Learning Using a (Recurrent) Neural Network—0
Evolution Strategies as a Scalable Alternative to Reinforcement LearningCode1
Sample Efficient Feature Selection for Factored MDPs—0
What can you do with a rock? Affordance extraction via word embeddings—0
Model-Agnostic Meta-Learning for Fast Adaptation of Deep NetworksCode1
Robust Adversarial Reinforcement LearningCode1
Tree-Structured Reinforcement Learning for Sequential Object Localization—0
Learning Invariant Feature Spaces to Transfer Skills with Reinforcement Learning—0
Deep Variation-structured Reinforcement Learning for Visual Relationship and Attribute DetectionCode0
Tactics of Adversarial Attack on Deep Reinforcement Learning Agents—0
Functions that Emerge through End-to-End Reinforcement Learning - The Direction for Artificial General Intelligence -—0
Neural Episodic ControlCode0
Surprise-Based Intrinsic Motivation for Deep Reinforcement Learning—0
Third-Person Imitation LearningCode0
Unsupervised Basis Function Adaptation for Reinforcement Learning—0
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction—0
FeUdal Networks for Hierarchical Reinforcement LearningCode0
Generalised Discount Functions applied to a Monte-Carlo AImu ImplementationCode0
Actor-Critic Reinforcement Learning with Simultaneous Human Control and Feedback—0
EX2: Exploration with Exemplar Models for Deep Reinforcement LearningCode0
Multi-step Reinforcement Learning: A Unifying Algorithm—0
A Laplacian Framework for Option Discovery in Reinforcement LearningCode0
Reinforcement Learning for Pivoting TaskCode0
Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless NavigationCode1
Learning to Optimize Neural Nets—0
Show, Attend and Interact: Perceivable Human-Robot Social Interaction through Neural Attention Q-Network—0
Analysing Congestion Problems in Multi-agent Reinforcement Learning—0
Analysis of Agent Expertise in Ms. Pac-Man using Value-of-Information-based Policies—0
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Benchmark Results

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