Deep Reinforcement Learning Agent

Deep reinforcement learning (DRL) agents are artificial intelligence systems that learn to make optimal decisions in complex environments through trial and error, aiming to maximize cumulative rewards. Current research emphasizes improving sample efficiency, addressing the "primacy bias" (over-reliance on early experiences), and enhancing explainability and generalizability across diverse tasks and unseen environments. Prominent approaches include Proximal Policy Optimization (PPO), transformer-based architectures, and ensemble methods, with a growing focus on incorporating techniques like curriculum learning and symbolic distillation to improve performance and interpretability. These advancements hold significant potential for applications in various fields, including finance, robotics, and game playing, while also contributing to a deeper understanding of learning and decision-making processes.

Papers

July 19, 2024

Explainable Post hoc Portfolio Management Financial Policy of a Deep Reinforcement Learning agent
Alejandra de la Rica Escudero, Eduardo C. Garrido-Merchan, Maria Coronado-Vaca
Deep Reinforcement Learning Deep Reinforcement Learning Agent Portfolio Optimization Model Explainable Deep Reinforcement Learning

June 17, 2024

Generalisation to unseen topologies: Towards control of biological neural network activity
Laurens Engwegen, Daan Brinks, Wendelin Böhmer
Strong Generalization External Control Generalization Performance Biological Neural Network Neuronal Network Deep Reinforcement Learning Agent

May 3, 2024

Proximal Curriculum with Task Correlations for Deep Reinforcement Learning
Georgios Tzannetos, Parameswaran Kamalaruban, Adish Singla
Reinforcement Learning Deep Reinforcement Learning Proximal Curriculum Deep Reinforcement Learning Agent Task Distribution Learner Model Task Correlation Curriculum Design

April 21, 2024

Adaptive Social Force Window Planner with Reinforcement Learning
Mauro Martini, Noé Pérez-Higueras, Andrea Ostuni, Marcello Chiaberge, Fernando Caballero, Luis Merino
Reinforcement Learning Social Navigation Autonomous Navigation System Deep Reinforcement Learning Agent Human Navigation

February 12, 2024

A Competition Winning Deep Reinforcement Learning Agent in microRTS
Scott Goodfriend
Deep Reinforcement Learning Imitation Learning Behavior Cloning Competition Platform Deep Reinforcement Learning Agent Real Time Strategy

December 18, 2023

The Animal-AI Environment: A Virtual Laboratory For Comparative Cognition and Artificial Intelligence Research
Konstantinos Voudouris, Ibrahim Alhas, Wout Schellaert, Matteo G. Mecattaf, Benjamin Slater, Matthew Crosby, Joel Holmes, John Burden, Niharika Chaubey, Niall Donnelly, Matishalin Patel, Marta Halina, José Hernández-Orallo, Lucy G. Cheke
Deep Reinforcement Learning Agent Behavioural Experiment

October 31, 2023

Sample-Efficient and Safe Deep Reinforcement Learning via Reset Deep Ensemble Agents
Woojun Kim, Yongjae Shin, Jongeui Park, Youngchul Sung
Deep Reinforcement Learning Safe Reinforcement Learning Regret Minimization Deep Reinforcement Learning Agent Based Reset

September 9, 2023

Verifiable Reinforcement Learning Systems via Compositionality
Cyrus Neary, Aryaman Singh Samyal, Christos Verginis, Murat Cubuktepe, Ufuk Topcu
Action Space Partial Observability Compositional Language Deep Reinforcement Learning Agent Compositional Reinforcement Learning

June 19, 2023

AdaStop: adaptive statistical testing for sound comparisons of Deep RL agents
Timothée Mathieu, Riccardo Della Vecchia, Alena Shilova, Matheus Medeiros Centa, Hector Kohler, Odalric-Ambrym Maillard, Philippe Preux
Research Reproducibility Deep RL Deep Reinforcement Learning Agent Adaptive Testing

June 16, 2023

Bootstrapped Representations in Reinforcement Learning
Charline Le Lan, Stephen Tu, Mark Rowland, Anna Harutyunyan, Rishabh Agarwal, Marc G. Bellemare, Will Dabney
Reinforcement Learning State Space State Representation Deep Reinforcement Learning Agent

May 1, 2023

Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition
Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Remi Munos, Will Dabney, Diana L Borsa
Deep Reinforcement Learning Representation Learning Meaningful Representation Environment Exploration Singular Value Decomposition Deep Reinforcement Learning Agent Exploration Problem Predictive State Representation

April 25, 2023

Proximal Curriculum for Reinforcement Learning Agents
Georgios Tzannetos, Bárbara Gomes Ribeiro, Parameswaran Kamalaruban, Adish Singla
Reinforcement Learning Reinforcement Learning Agent Proximal Curriculum Deep Reinforcement Learning Agent Curriculum Design

March 13, 2023

Loss of Plasticity in Continual Deep Reinforcement Learning
Zaheer Abbas, Rosie Zhao, Joseph Modayil, Adam White, Marlos C. Machado
Continual LEArning Interest Loss Plasticity Rule Continual Reinforcement Learning Deep Reinforcement Learning Agent Value Based Deep Reinforcement Learning

February 18, 2023

Efficient Exploration via Epistemic-Risk-Seeking Policy Optimization
Brendan O'Donoghue
Deep Reinforcement Learning Policy OpTimization Efficient Exploration Deep Reinforcement Learning Agent Optimistic Gradient

November 17, 2022

Explainability Via Causal Self-Talk
Nicholas A. Roy, Junkyung Kim, Neil Rabinowitz
AI System Artificial Intelligence System Deep Reinforcement Learning Agent

October 24, 2022

Symbolic Distillation for Learned TCP Congestion Control
S P Sharan, Wenqing Zheng, Kuo-Feng Hsu, Jiarong Xing, Ang Chen, Zhangyang Wang
Deep Reinforcement Learning Knowledge Distillation Deep Reinforcement Learning Agent Congestion Control Symbolic Task

October 22, 2022

Probing Transfer in Deep Reinforcement Learning without Task Engineering
Andrei A. Rusu, Sebastian Flennerhag, Dushyant Rao, Razvan Pascanu, Raia Hadsell
Deep Reinforcement Learning Generalization Performance Deep Reinforcement Linear Probing Task Specification Game Design Deep Reinforcement Learning Agent ATARI Game

August 9, 2022

Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2
Zachary Daniels, Aswin Raghavan, Jesse Hostetler, Abrar Rahman, Indranil Sur, Michael Piacentino, Ajay Divakaran
Application Proficiency Deep RL Generative Replay Deep Reinforcement Learning Agent Lifelong Reinforcement Learning StarCraft II

June 5, 2022

Learning Dynamics and Generalization in Reinforcement Learning
Clare Lyle, Mark Rowland, Will Dabney, Marta Kwiatkowska, Yarin Gal
Reinforcement Learning Strong Generalization Policy Gradient Learning Dynamic Temporal Difference Learning Deep Reinforcement Learning Agent

May 18, 2022

Generating Explanations from Deep Reinforcement Learning Using Episodic Memory
Sam Blakeman, Denis Mareschal
Deep Reinforcement Learning Explanation Generation Episodic Memory Deep Reinforcement Learning Agent