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

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Showing 6026–6050 of 15113 papers

TitleStatusHype
Multi-trainer Interactive Reinforcement Learning System—0
Output Feedback Adaptive Optimal Control of Affine Nonlinear systems with a Linear Measurement Model—0
Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics BeliefCode0
Personalized Federated Hypernetworks for Privacy Preservation in Multi-Task Reinforcement Learning—0
Object-Category Aware Reinforcement Learning—0
Towards Multi-Agent Reinforcement Learning driven Over-The-Counter Market Simulations—0
Reinforcement Learning with Unbiased Policy Evaluation and Linear Function Approximation—0
Optimal Control of Material Micro-Structures—0
Policy Gradient With Serial Markov Chain Reasoning—0
Observed Adversaries in Deep Reinforcement Learning—0
Deep reinforcement learning for automatic run-time adaptation of UWB PHY radio settings—0
Bootstrap Advantage Estimation for Policy Optimization in Reinforcement LearningCode0
Dissipative residual layers for unsupervised implicit parameterization of data manifolds—0
Efficient circuit implementation for coined quantum walks on binary trees and application to reinforcement learning—0
A Concise Introduction to Reinforcement Learning in Robotics—0
Causality-driven Hierarchical Structure Discovery for Reinforcement Learning—0
Centralized Training with Hybrid Execution in Multi-Agent Reinforcement LearningCode0
Explaining Online Reinforcement Learning Decisions of Self-Adaptive Systems—0
DQLAP: Deep Q-Learning Recommender Algorithm with Update Policy for a Real Steam Turbine System—0
A Unified Framework for Alternating Offline Model Training and Policy LearningCode0
Smooth Trajectory Collision Avoidance through Deep Reinforcement Learning—0
Reinforcement Learning with Automated Auxiliary Loss Search—0
Real World Offline Reinforcement Learning with Realistic Data Source—0
Regret Bounds for Risk-Sensitive Reinforcement Learning—0
Multi-User Reinforcement Learning with Low Rank Rewards—0
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Benchmark Results

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