Learning Agent

Learning agents are artificial intelligence systems that learn to make decisions and achieve goals through interaction with their environment, often employing reinforcement learning techniques. Current research emphasizes improving agent performance through methods like policy space response oracles (PSRO) for multi-agent systems, maximally permissive reward machines for efficient learning, and adaptive incentive designs to guide behavior towards socially optimal outcomes. This field is crucial for advancing AI safety, improving decision-making in complex systems (e.g., economics, healthcare), and developing more interpretable and verifiable AI agents for real-world applications.

Papers

November 28, 2023

Agent-Aware Training for Agent-Agnostic Action Advising in Deep Reinforcement Learning
Yaoquan Wei, Shunyu Liu, Jie Song, Tongya Zheng, Kaixuan Chen, Yong Wang, Mingli Song
Deep Reinforcement Learning Learning Agent Generalist Agent Agent Training

October 30, 2023

Meta-Learning Strategies through Value Maximization in Neural Networks
Rodrigo Carrasco-Davis, Javier Masís, Andrew M. Saxe
Neural Network Meta Learning Resource Allocation Learning Agent Biological Insight Cognitive Control

September 20, 2023

Adaptive Multi-Agent Deep Reinforcement Learning for Timely Healthcare Interventions
Thanveer Shaik, Xiaohui Tao, Lin Li, Haoran Xie, Hong-Ning Dai, Feng Zhao, Jianming Yong
Adaptive Importance Multi Agent Deep Reinforcement Learning Health Monitoring Learning Agent Patient Monitoring Medical Intervention

July 13, 2023

Prescriptive Process Monitoring Under Resource Constraints: A Reinforcement Learning Approach
Mahmoud Shoush, Marlon Dumas
Reinforcement Learning Reinforcement Learning Approach Learning Agent Resource Constraint Prescriptive Process Monitoring

July 10, 2023

On the Computational Modeling of Meaning: Embodied Cognition Intertwined with Emotion
Casey Kennington
Underlying Emotion Multiple Meaning Computational Model Cognitive Learning Learning Agent Language Learning

June 1, 2023

EMOTE: An Explainable architecture for Modelling the Other Through Empathy
Manisha Senadeera, Thommen Karimpanal George, Sunil Gupta, Stephan Jacobs, Santu Rana
Multi Agent Cognitive Empathy Learning Agent Multi Agent Scenario Body Gesture Interpretable Architecture

May 4, 2023

Maximum Causal Entropy Inverse Constrained Reinforcement Learning
Mattijs Baert, Pietro Mazzaglia, Sam Leroux, Pieter Simoens
Inverse Reinforcement Learning Artificial Agent State Action Space Learning Agent Causal Entropy Cost Function

April 25, 2023

Centralized control for multi-agent RL in a complex Real-Time-Strategy game
Roger Creus Castanyer
Multi Agent Reinforcement Learning Learning Agent Centralized Control RL Agent Real Time Strategy Multi Agent RL Single Agent RL

April 4, 2023

Optimal Transport for Correctional Learning
Rebecka Winqvist, Inês Lourenco, Francesco Quinzan, Cristian R. Rojas, Bo Wahlberg
Optimal Transport Inverse Reinforcement Learning Learning Agent

March 2, 2023

Computational Language Acquisition with Theory of Mind
Andy Liu, Hao Zhu, Emmy Liu, Yonatan Bisk, Graham Neubig
Theoretical Understanding Human Mind Learning Agent Spoken Utterance Language Acquisition

February 23, 2023

K-SHAP: Policy Clustering Algorithm for Anonymous Multi-Agent State-Action Pairs
Andrea Coletta, Svitlana Vyetrenko, Tucker Balch
Imitation Learning Agent Behavior Learning Agent Fusion SHAP Agent Strategy Policy Similarity

December 16, 2022

Safe Evaluation For Offline Learning: Are We Ready To Deploy?
Hager Radi, Josiah P. Hanna, Peter Stone, Matthew E. Taylor
Reinforcement Learning Policy Evaluation Learning Agent Realistic Evaluation Target Policy

November 19, 2022

PIC4rl-gym: a ROS2 modular framework for Robots Autonomous Navigation with Deep Reinforcement Learning
Mauro Martini, Andrea Eirale, Simone Cerrato, Marcello Chiaberge
Deep Reinforcement Learning Autonomous Navigation Robot Operating System Modular Framework Learning Agent Autonomous Robot Navigation Robotics Community MR iNet Gym Outdoor Navigation

November 10, 2022

Reinforcement Learning in an Adaptable Chess Environment for Detecting Human-understandable Concepts
Patrik Hammersborg, Inga Strümke
Reinforcement Learning Deep Neural Network Autonomous Vehicle Autonomous Agent Learning Agent Concept Based Explanation Chess Engine

August 30, 2022

Correct-by-Construction Runtime Enforcement in AI -- A Survey
Bettina Könighofer, Roderick Bloem, Rüdiger Ehlers, Christian Pek
Artificial Intelligence Timely Survey Action Space Learning Agent

June 28, 2022

Dynamic Memory for Interpretable Sequential Optimisation
Srivas Chennu, Andrew Maher, Jamie Martin, Subash Prabanantham
Reinforcement Learning Learning Agent Bandit Problem Dynamic Memory Adaptive Bayesian Interpretable Optimization

April 7, 2022

Automatic Parameter Optimization Using Genetic Algorithm in Deep Reinforcement Learning for Robotic Manipulation Tasks
Adarsh Sehgal, Nicholas Ward, Hung La, Sushil Louis
Reinforcement Learning Deep Reinforcement Learning Genetic Algorithm Manipulation Task Deep Deterministic Policy Gradient Learning Agent Hindsight Experience Replay Parameter Optimization

February 21, 2022

February 20, 2022

Cooperative Artificial Intelligence
Tobias Baumann
Multi Agent Reinforcement Learning Game Theory Learning Agent Cooperative AI