Model Based Reinforcement Learning

Model-based reinforcement learning (MBRL) aims to improve the sample efficiency and robustness of reinforcement learning agents by learning a model of the environment's dynamics. Current research focuses on enhancing model accuracy and robustness through techniques like incorporating expert knowledge, using bisimulation metrics for state representation, and employing adversarial training to handle uncertainties. These advancements are leading to more efficient and reliable control policies in various applications, including robotics, autonomous driving, and even protein design, by reducing the need for extensive real-world interactions during training.

Papers

August 26, 2024

Model-Based Reinforcement Learning for Control of Strongly-Disturbed Unsteady Aerodynamic Flows
Zhecheng Liu, Diederik Beckers, Jeff D. Eldredge
Reinforcement Learning Deep Reinforcement Learning External Control Model Based Reinforcement Learning Aerodynamic Force Gust Alleviation

August 20, 2024

Offline Model-Based Reinforcement Learning with Anti-Exploration
Padmanaba Srinivasan, William Knottenbelt
Offline Reinforcement Learning Model Based Reinforcement Learning Action Free Offline

August 16, 2024

Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds
Zhiyong Wang, Dongruo Zhou, John C.S. Lui, Wen Sun
Markov Decision Process Model Based Reinforcement Learning Regret Analysis New Horizon Trajectory Wise Reward

August 4, 2024

Scalable Signal Temporal Logic Guided Reinforcement Learning via Value Function Space Optimization
Yiting He, Peiran Liu, Yiding Ji
Reinforcement Learning Model Based Reinforcement Learning Signal Temporal Logic Goal Conditioned Reinforcement Learning Space Optimality

July 28, 2024

The Interpretability of Codebooks in Model-Based Reinforcement Learning is Limited
Kenneth Eaton, Jonathan Balloch, Julia Kim, Mark Riedl
Deep Reinforcement Learning Inherent Interpretability Model Based Reinforcement Learning Model Interpretability Codebook Learning Interpretability Analysis Concept Disentanglement

July 18, 2024

Model-based Policy Optimization using Symbolic World Model
Andrey Gorodetskiy, Konstantin Mironov, Aleksandr Panov
Learning Based Model Based Reinforcement Learning Symbolic Model Model Based Policy

July 15, 2024

BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning
Haohong Lin, Wenhao Ding, Jian Chen, Laixi Shi, Jiacheng Zhu, Bo Li, Ding Zhao
Model Based Reinforcement Learning Policy Learning Causal Representation Model Mismatch

July 12, 2024

GNN with Model-based RL for Multi-agent Systems
Hanxiao Chen
Multi Agent System Predictive Control Model Based Reinforcement Learning Gene Level GNN Multiple Agent

July 6, 2024

FOSP: Fine-tuning Offline Safe Policy through World Models
Chenyang Cao, Yucheng Xin, Silang Wu, Longxiang He, Zichen Yan, Junbo Tan, Xueqian Wang
Model Based Reinforcement Learning World Model Offline Policy Model Based Safe Reinforcement Learning

June 23, 2024

Bounding-Box Inference for Error-Aware Model-Based Reinforcement Learning
Erin J. Talvitie, Zilei Shao, Huiying Li, Jinghan Hu, Jacob Boerma, Rory Zhao, Xintong Wang
Reinforcement Learning Model Based Reinforcement Learning Policy Learning Efficient Planning

June 19, 2024

Infinite-Horizon Reinforcement Learning with Multinomial Logistic Function Approximation
Jaehyun Park, Junyeop Kwon, Dabeen Lee
Reinforcement Learning Markov Decision Process Model Based Reinforcement Learning Episodic Markov Decision Process Multinomial Logistic

June 11, 2024

Optimal Gait Control for a Tendon-driven Soft Quadruped Robot by Model-based Reinforcement Learning
Xuezhi Niu, Kaige Tan, Lei Feng
Model Based Reinforcement Learning Multiple Gait Optimal Gait Soft Legged Robot

June 10, 2024

Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation
Michelle Pan, Mariah Schrum, Vivek Myers, Erdem Bıyık, Anca Dragan
Model Based Reinforcement Learning

June 4, 2024

Reinforcement learning-based architecture search for quantum machine learning
Frederic Rapp, David A. Kreplin, Marco F. Huber, Marco Roth
Reinforcement Learning Quantum Machine Learning Model Based Reinforcement Learning Positive Reinforcement Quantum Machine Learning Model Architecture Search Digital Circuit

June 3, 2024

May 29, 2024

Trust the Model Where It Trusts Itself -- Model-Based Actor-Critic with Uncertainty-Aware Rollout Adaption
Bernd Frauenknecht, Artur Eisele, Devdutt Subhasish, Friedrich Solowjow, Sebastian Trimpe
Full Model Model Based Reinforcement Learning Appropriate Trust Model Free Actor Critic Model Rollouts

May 25, 2024

Safe Deep Model-Based Reinforcement Learning with Lyapunov Functions
Harry Zhang
Predictive Control Model Based Reinforcement Learning Lyapunov Function Safe Deep

May 24, 2024

Model Based Reinforcement Learning

Papers

Model-Based Reinforcement Learning for Control of Strongly-Disturbed Unsteady Aerodynamic Flows

Offline Model-Based Reinforcement Learning with Anti-Exploration

Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds

Scalable Signal Temporal Logic Guided Reinforcement Learning via Value Function Space Optimization

The Interpretability of Codebooks in Model-Based Reinforcement Learning is Limited

Model-based Policy Optimization using Symbolic World Model

BECAUSE: Bilinear Causal Representation for Generalizable Offline Model-based Reinforcement Learning

GNN with Model-based RL for Multi-agent Systems

FOSP: Fine-tuning Offline Safe Policy through World Models

Bounding-Box Inference for Error-Aware Model-Based Reinforcement Learning

Infinite-Horizon Reinforcement Learning with Multinomial Logistic Function Approximation

Optimal Gait Control for a Tendon-driven Soft Quadruped Robot by Model-based Reinforcement Learning

Coprocessor Actor Critic: A Model-Based Reinforcement Learning Approach For Adaptive Brain Stimulation

Reinforcement learning-based architecture search for quantum machine learning

A New View on Planning in Online Reinforcement Learning

Adaptive Layer Splitting for Wireless LLM Inference in Edge Computing: A Model-Based Reinforcement Learning Approach

Trust the Model Where It Trusts Itself -- Model-Based Actor-Critic with Uncertainty-Aware Rollout Adaption

Safe Deep Model-Based Reinforcement Learning with Lyapunov Functions

Neuromorphic dreaming: A pathway to efficient learning in artificial agents

Generating Code World Models with Large Language Models Guided by Monte Carlo Tree Search