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 13251–13300 of 15113 papers

TitleStatusHype
Quality-Aware Multimodal Saliency Detection via Deep Reinforcement Learning—0
Understanding the impact of entropy on policy optimizationCode0
Automatic Face Aging in Videos via Deep Reinforcement Learning—0
Distributed traffic light control at uncoupled intersections with real-world topology by deep reinforcement learning—0
Grammars and reinforcement learning for molecule optimizationCode0
Learning State Representations in Complex Systems with Multimodal Data—0
PNS: Population-Guided Novelty Search for Reinforcement Learning in Hard Exploration Environments—0
Environments for Lifelong Reinforcement LearningCode0
Genetic-Gated Networks for Deep Reinforcement—0
Reinforcement Learning for Uplift ModelingCode0
Reinforced Cross-Modal Matching and Self-Supervised Imitation Learning for Vision-Language Navigation—0
A Model-Based Reinforcement Learning Approach for a Rare Disease Diagnostic Task—0
Learning to Activate Relay Nodes: Deep Reinforcement Learning Approach—0
Model-Based Reinforcement Learning for Sepsis Treatment—0
TorchProteinLibrary: A computationally efficient, differentiable representation of protein structureCode0
Learning Goal Embeddings via Self-Play for Hierarchical Reinforcement LearningCode0
Integrating Task-Motion Planning with Reinforcement Learning for Robust Decision Making in Mobile Robots—0
High-Level Strategy Selection under Partial Observability in StarCraft: Brood War—0
Integrating Reinforcement Learning to Self Training for Pulmonary Nodule Segmentation in Chest X-rays—0
Urban Driving with Multi-Objective Deep Reinforcement LearningCode0
Neural Machine Translation with Adequacy-Oriented Learning—0
Model Learning for Look-ahead Exploration in Continuous ControlCode0
Reinforcement Learning of Active Vision for Manipulating Objects under OcclusionsCode0
Scalable agent alignment via reward modeling: a research directionCode0
Simulated Autonomous Driving in a Realistic Driving Environment using Deep Reinforcement Learning and a Deterministic Finite State Machine—0
Reinforcement Learning with A* and a Deep HeuristicCode0
Reinforcement Learning and Inverse Reinforcement Learning with System 1 and System 2—0
Measurement-based adaptation protocol with quantum reinforcement learning in a Rigetti quantum computer—0
Energy Efficiency in Reinforcement Learning for Wireless Sensor Networks—0
Learning Actionable Representations with Goal-Conditioned PoliciesCode0
Self-Organizing Maps for Storage and Transfer of Knowledge in Reinforcement Learning—0
Policy Optimization with Model-based Explorations—0
Recursive Sparse Pseudo-input Gaussian Process SARSA—0
Parameter Sharing Reinforcement Learning Architecture for Multi Agent Driving Behaviors—0
Autonomous Extraction of a Hierarchical Structure of Tasks in Reinforcement Learning, A Sequential Associate Rule Mining Approach—0
Emergence of linguistic conventions in multi-agent reinforcement learning—0
Improving Automatic Source Code Summarization via Deep Reinforcement LearningCode0
Intervention Aided Reinforcement Learning for Safe and Practical Policy Optimization in Navigation—0
Concept Learning through Deep Reinforcement Learning with Memory-Augmented Neural Networks—0
Reward learning from human preferences and demonstrations in AtariCode0
Tight Bayesian Ambiguity Sets for Robust MDPs—0
Orthogonal Policy Gradient and Autonomous Driving Application—0
The Utility of Sparse Representations for Control in Reinforcement Learning—0
Natural Environment Benchmarks for Reinforcement LearningCode0
Bayesian Reinforcement Learning in Factored POMDPs—0
Large-scale Interactive Recommendation with Tree-structured Policy Gradient—0
Emergence of Addictive Behaviors in Reinforcement Learning Agents—0
Deep Q learning for fooling neural networksCode0
Image Captioning Based on a Hierarchical Attention Mechanism and Policy Gradient Optimization—0
Modelling the Dynamic Joint Policy of Teammates with Attention Multi-agent DDPG—0
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Benchmark Results

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