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 10826–10850 of 15113 papers

TitleStatusHype
Solving the scalarization issues of Advantage-based Reinforcement Learning AlgorithmsCode0
Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward DecompositionCode1
Monte-Carlo Siamese Policy on Actor for Satellite Image Super Resolution—0
Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approach—0
An Application of Deep Reinforcement Learning to Algorithmic TradingCode1
Guided Dialog Policy Learning without Adversarial Learning in the LoopCode0
Online Constrained Model-based Reinforcement Learning—0
Optimistic Agent: Accurate Graph-Based Value Estimation for More Successful Visual Navigation—0
Uniform State Abstraction For Reinforcement Learning—0
Technical Report: Adaptive Control for Linearizable Systems Using On-Policy Reinforcement Learning—0
Networked Multi-Agent Reinforcement Learning with Emergent Communication—0
Weakly-Supervised Reinforcement Learning for Controllable Behavior—0
Intrinsic Exploration as Multi-Objective RL—0
Adaptive Partial Scanning Transmission Electron Microscopy with Reinforcement LearningCode0
Multi-agent Reinforcement Learning for Resource Allocation in IoT networks with Edge Computing—0
Reinforcement Learning Architectures: SAC, TAC, and ESAC—0
Stylistic Dialogue Generation via Information-Guided Reinforcement Learning Strategy—0
Reinforced Multi-task Approach for Multi-hop Question Generation—0
MRI Reconstruction with Interpretable Pixel-Wise Operations Using Reinforcement LearningCode1
Reinforcement Learning for Mixed-Integer Problems Based on MPC—0
Multi-agent Reinforcement Learning for Networked System Control—0
Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement LearningCode1
A Deep Ensemble Multi-Agent Reinforcement Learning Approach for Air Traffic Control—0
Continuous Motion Planning with Temporal Logic Specifications using Deep Neural Networks—0
Average Reward Adjusted Discounted Reinforcement Learning: Near-Blackwell-Optimal Policies for Real-World Applications—0
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Benchmark Results

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