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 13051–13100 of 15113 papers

TitleStatusHype
Total stochastic gradient algorithms and applications in reinforcement learning—0
Learning to Schedule Communication in Multi-agent Reinforcement LearningCode0
AlphaStar: An Evolutionary Computation Perspective—0
Interactively shaping robot behaviour with unlabeled human instructions—0
Adaptive Stress Testing for Autonomous Vehicles—0
The Natural Language of ActionsCode0
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of ChaosCode0
Value-aware Recommendation based on Reinforced Profit Maximization in E-commerce Systems—0
A Meta-MDP Approach to Exploration for Lifelong Reinforcement LearningCode0
Learning User Preferences via Reinforcement Learning with Spatial Interface Valuing—0
When Collaborative Filtering Meets Reinforcement Learning—0
Non-asymptotic Analysis of Biased Stochastic Approximation Scheme—0
Policy Consolidation for Continual Reinforcement LearningCode0
Visual Rationalizations in Deep Reinforcement Learning for Atari Games—0
Privacy Preserving Off-Policy Evaluation—0
Learning Action Representations for Reinforcement Learning—0
Competitive Experience Replay—0
Joint Entity Linking with Deep Reinforcement Learning—0
An Optimization Framework for Task Sequencing in Curriculum Learning—0
A Geometric Perspective on Optimal Representations for Reinforcement Learning—0
Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization PerspectiveCode0
Tsallis Reinforcement Learning: A Unified Framework for Maximum Entropy Reinforcement Learning—0
Successor Features Combine Elements of Model-Free and Model-based Reinforcement Learning—0
The Value Function Polytope in Reinforcement Learning—0
Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory GANsCode0
Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning—0
Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement—0
Privacy-preserving Q-Learning with Functional Noise in Continuous State SpacesCode0
A Comparative Analysis of Expected and Distributional Reinforcement Learning—0
A Regulation Enforcement Solution for Multi-agent Reinforcement Learning—0
Self-organization of action hierarchy and compositionality by reinforcement learning with recurrent neural networksCode0
Multi-Agent Reinforcement Learning with Multi-Step Generative Models—0
Safe, Efficient, and Comfortable Velocity Control based on Reinforcement Learning for Autonomous DrivingCode0
Trust Region-Guided Proximal Policy OptimizationCode0
Designing a Multi-Objective Reward Function for Creating Teams of Robotic Bodyguards Using Deep Reinforcement Learning—0
CLIC: Curriculum Learning and Imitation for object Control in non-rewarding environments—0
Off-Policy Deep Reinforcement Learning by Bootstrapping the Covariate Shift—0
Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning—0
Reward Shaping via Meta-Learning—0
Probabilistic Recursive Reasoning for Multi-Agent Reinforcement Learning—0
Action Robust Reinforcement Learning and Applications in Continuous ControlCode0
Emergent Linguistic Phenomena in Multi-Agent Communication GamesCode0
Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization—0
Sample Complexity of Estimating the Policy Gradient for Nearly Deterministic Dynamical Systems—0
Federated Deep Reinforcement Learning—0
Feudal Multi-Agent Hierarchies for Cooperative Reinforcement Learning—0
Dynamic Measurement Scheduling for Event Forecasting using Deep RLCode0
Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based roboticsCode0
Hierarchical Reinforcement Learning for Multi-agent MOBA Game—0
Distillation Strategies for Proximal Policy Optimization—0
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Benchmark Results

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