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 13151–13200 of 15113 papers

TitleStatusHype
Model Learning for Look-ahead Exploration in Continuous ControlCode0
Reinforcement Learning of Active Vision for Manipulating Objects under OcclusionsCode0
Energy Efficiency in Reinforcement Learning for Wireless Sensor Networks—0
Reinforcement Learning and Inverse Reinforcement Learning with System 1 and System 2—0
Simulated Autonomous Driving in a Realistic Driving Environment using Deep Reinforcement Learning and a Deterministic Finite State Machine—0
Measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti quantum computer—0
Practical Deep Reinforcement Learning Approach for Stock TradingCode3
Scalable agent alignment via reward modeling: a research directionCode0
Reinforcement Learning with A* and a Deep HeuristicCode0
Learning Actionable Representations with Goal-Conditioned PoliciesCode0
Policy Optimization with Model-based Explorations—0
Self-Organizing Maps for Storage and Transfer of Knowledge in Reinforcement Learning—0
Recursive Sparse Pseudo-input Gaussian Process SARSA—0
Parameter Sharing Reinforcement Learning Architecture for Multi Agent Driving Behaviors—0
Emergence of linguistic conventions in multi-agent reinforcement learning—0
Improving Automatic Source Code Summarization via Deep Reinforcement LearningCode0
Autonomous Extraction of a Hierarchical Structure of Tasks in Reinforcement Learning, A Sequential Associate Rule Mining Approach—0
Concept Learning through Deep Reinforcement Learning with Memory-Augmented Neural Networks—0
Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation—0
Orthogonal Policy Gradient and Autonomous Driving Application—0
Reward learning from human preferences and demonstrations in AtariCode0
Tight Bayesian Ambiguity Sets for Robust MDPs—0
The Utility of Sparse Representations for Control in Reinforcement Learning—0
Natural Environment Benchmarks for Reinforcement LearningCode0
Large-scale Interactive Recommendation with Tree-structured Policy Gradient—0
Bayesian Reinforcement Learning in Factored POMDPs—0
Emergence of Addictive Behaviors in Reinforcement Learning Agents—0
Modelling the Dynamic Joint Policy of Teammates with Attention Multi-agent DDPG—0
Image Captioning Based on a Hierarchical Attention Mechanism and Policy Gradient Optimization—0
Deep Q learning for fooling neural networksCode0
Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning—0
Learning Temporal Point Processes via Reinforcement Learning—0
Navigating Assistance System for Quadcopter with Deep Reinforcement Learning—0
Importance Weighted Evolution Strategies—0
Learning data augmentation policies using augmented random searchCode0
An initial attempt of combining visual selective attention with deep reinforcement learning—0
An Optimal Control View of Adversarial Machine Learning—0
Optimizing Taxi Carpool Policies via Reinforcement Learning and Spatio-Temporal Mining—0
Towards Governing Agent's Efficacy: Action-Conditional β-VAE for Deep Transparent Reinforcement Learning—0
Product Title Refinement via Multi-Modal Generative Adversarial Learning—0
Reinforcement Learning Based Speech Enhancement for Robust Speech Recognition—0
Diversity-Driven Extensible Hierarchical Reinforcement LearningCode0
Fully Convolutional Network with Multi-Step Reinforcement Learning for Image ProcessingCode0
Learning Shaping Strategies in Human-in-the-loop Interactive Reinforcement Learning—0
Reinforcement Learning for Automatic Test Case Prioritization and Selection in Continuous IntegrationCode0
A Hierarchical Framework for Relation Extraction with Reinforcement LearningCode0
Correlation Filter Selection for Visual Tracking Using Reinforcement Learning—0
Modular Architecture for StarCraft II with Deep Reinforcement Learning—0
Memory-based Deep Reinforcement Learning for Obstacle Avoidance in UAV with Limited Environment KnowledgeCode0
Meta-Learning for Multi-objective Reinforcement Learning—0
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Benchmark Results

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