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 9501–9550 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Backup Strategies against Adversaries—0
Discovery of Options via Meta-Learned Subgoals—0
Disturbing Reinforcement Learning Agents with Corrupted Rewards—0
Hedging of Financial Derivative Contracts via Monte Carlo Tree Search—0
Deep Reinforcement Learning for Portfolio Optimization using Latent Feature State Space (LFSS) Module—0
Deep Reinforcement Learning for Combinatorial Optimization: Covering Salesman Problems—0
Representation Matters: Offline Pretraining for Sequential Decision Making—0
Risk-Averse Bayes-Adaptive Reinforcement Learning—0
Reinforcement Learning for Optimized Beam Training in Multi-Hop Terahertz Communications—0
Non-stationary Reinforcement Learning without Prior Knowledge: An Optimal Black-box Approach—0
Simple Agent, Complex Environment: Efficient Reinforcement Learning with Agent States—0
Patterns, predictions, and actions: A story about machine learning—0
Modeling the Interaction between Agents in Cooperative Multi-Agent Reinforcement Learning—0
Policy Augmentation: An Exploration Strategy for Faster Convergence of Deep Reinforcement Learning AlgorithmsCode0
Personalization for Web-based Services using Offline Reinforcement Learning—0
Derivative-Free Reinforcement Learning: A Review—0
Defense Against Reward Poisoning Attacks in Reinforcement Learning—0
Leveraging Reinforcement Learning for evaluating Robustness of KNN Search Algorithms—0
Learning Equational Theorem Proving—0
Deep Reinforcement Learning with Symmetric Prior for Predictive Power Allocation to Mobile Users—0
Learning State Representations from Random Deep Action-conditional PredictionsCode0
Adaptive Pairwise Weights for Temporal Credit Assignment—0
Scheduling the NASA Deep Space Network with Deep Reinforcement Learning—0
Measuring Progress in Deep Reinforcement Learning Sample Efficiency—0
Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature—0
Unlocking Pixels for Reinforcement Learning via Implicit Attention—0
Neurogenetic Programming Framework for Explainable Reinforcement LearningCode0
Generate and Revise: Reinforcement Learning in Neural Poetry—0
Contrasting Centralized and Decentralized Critics in Multi-Agent Reinforcement Learning—0
Introduction to Machine Learning for the Sciences—0
Learning Optimal Strategies for Temporal Tasks in Stochastic Games—0
An Analysis of Frame-skipping in Reinforcement Learning—0
Sparsely ensembled convolutional neural network classifiers via reinforcement learningCode0
Multi-Agent Deep Reinforcement Learning for Request Dispatching in Distributed-Controller Software-Defined Networking—0
A bandit approach to curriculum generation for automatic speech recognition—0
Improving Model and Search for Computer Go—0
MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management—0
A Hybrid Approach for Reinforcement Learning Using Virtual Policy Gradient for Balancing an Inverted Pendulum—0
Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency—0
Experience-Based Heuristic Search: Robust Motion Planning with Deep Q-Learning—0
Deceptive Reinforcement Learning for Privacy-Preserving Planning—0
Addressing Inherent Uncertainty: Risk-Sensitive Behavior Generation for Automated Driving using Distributional Reinforcement Learning—0
Deep reinforcement learning for smart calibration of radio telescopesCode0
Revisiting Prioritized Experience Replay: A Value PerspectiveCode0
Provably Efficient Algorithms for Multi-Objective Competitive RL—0
Persistent Rule-based Interactive Reinforcement Learning—0
Hybrid Adversarial Imitation Learning—0
A review of motion planning algorithms for intelligent robotics—0
Deep reinforcement learning-based image classification achieves perfect testing set accuracy for MRI brain tumors with a training set of only 30 images—0
How to Train Your Robot with Deep Reinforcement Learning; Lessons We've Learned—0
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Benchmark Results

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