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 11701–11725 of 15113 papers

TitleStatusHype
Which Channel to Ask My Question? Personalized Customer Service RequestStream Routing using DeepReinforcement Learning—0
Scaling active inference—0
Corpus-Level End-to-End Exploration for Interactive SystemsCode0
Dynamic Control of a Fiber Manufacturing Process using Deep Reinforcement LearningCode0
Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learning—0
From Persistent Homology to Reinforcement Learning with Applications for Retail Banking—0
Fleet Control using Coregionalized Gaussian Process Policy IterationCode0
Analysis of Evolutionary Behavior in Self-Learning Media Search Engines—0
Graph Pruning for Model Compression—0
Deep Reinforcement Learning for Trading—0
DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement LearningCode0
Information-Theoretic Confidence Bounds for Reinforcement Learning—0
Efficient Drone Mobility Support Using Reinforcement Learning—0
Accelerating Reinforcement Learning with Suboptimal Guidance—0
Agent Probing Interaction Policies—0
Memory-Efficient Episodic Control Reinforcement Learning with Dynamic Online k-meansCode0
State Alignment-based Imitation Learning—0
Sample-Efficient Reinforcement Learning with Maximum Entropy Mellowmax Episodic ControlCode0
Solving Online Threat Screening Games using Constrained Action Space Reinforcement Learning—0
Safe Policies for Reinforcement Learning via Primal-Dual Methods—0
On Policy Learning Robust to Irreversible Events: An Application to Robotic In-Hand Manipulation—0
Deep Reinforcement Learning in Cryptocurrency Market Making—0
Hierarchical Average Reward Policy Gradient Algorithms—0
Avoiding Jammers: A Reinforcement Learning Approach—0
A Tale of Two-Timescale Reinforcement Learning with the Tightest Finite-Time Bound—0
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Benchmark Results

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