Stochastic Environment

Stochastic environments, characterized by unpredictable state transitions and outcomes, pose significant challenges for reinforcement learning (RL) and control systems. Current research focuses on developing robust algorithms and models, such as distributional RL, model-based methods incorporating uncertainty estimates, and hierarchical approaches, to address the limitations of traditional RL in these settings. This work is crucial for advancing the reliability and safety of AI agents in real-world applications, including autonomous driving, robotics, and resource management in dynamic systems, where uncertainty is inherent. The development of provably robust policies and efficient exploration strategies remains a key focus.

Papers

October 4, 2024

Model-Based Reward Shaping for Adversarial Inverse Reinforcement Learning in Stochastic Environments
Simon Sinong Zhan, Qingyuan Wu, Philip Wang, Yixuan Wang, Ruochen Jiao, Chao Huang, Qi Zhu
Reward Shaping Stochastic Environment Adversarial Inverse Reinforcement Learning

September 25, 2024

Offline and Distributional Reinforcement Learning for Radio Resource Management
Eslam Eldeeb, Hirley Alves
Reinforcement Learning Distributional Reinforcement Learning Action Free Offline Stochastic Environment Radio Resource Management Future Network

September 11, 2024

A Survey of Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges
Guiliang Liu, Sheng Xu, Shicheng Liu, Ashish Gaurav, Sriram Ganapathi Subramanian, Pascal Poupart
Timely Survey Autonomous Driving Technical Challenge Much Progress Robot Control Definition Defining Redefinition Stochastic Environment Knowledge Constraint Inverse Constrained Reinforcement Learning

August 6, 2024

Certifiably Robust Policies for Uncertain Parametric Environments
Yannik Schnitzer, Alessandro Abate, David Parker
Robust Policy Stochastic Environment POMDP Policy Uncertain Dynamic Environment Interval Markov Decision Process

June 6, 2024

Multifidelity digital twin for real-time monitoring of structural dynamics in aquaculture net cages
Eirini Katsidoniotaki, Biao Su, Eleni Kelasidi, Themistoklis P. Sapsis
Digital Twin Underwater Environment Stochastic Environment Structural Dynamic Aquaculture System Net CAGE Real Time Monitoring

April 24, 2024

Recursive Backwards Q-Learning in Deterministic Environments
Jan Diekhoff, Jörn Fischer
Reinforcement Learning Q Learning Deterministic Algorithm Recursive Algorithm Stochastic Environment Maze Environment

April 15, 2024

A Note on Loss Functions and Error Compounding in Model-based Reinforcement Learning
Nan Jiang
Loss Function Model Based Reinforcement Learning Short Note Deep RL Stochastic Environment

March 28, 2024

Learning a Formally Verified Control Barrier Function in Stochastic Environment
Manan Tayal, Hongchao Zhang, Pushpak Jagtap, Andrew Clark, Shishir Kolathaya
Autonomous Driving Obstacle Avoidance Control Barrier Function Stochastic Environment Neural Control Barrier Function

March 27, 2024

Preference-Based Planning in Stochastic Environments: From Partially-Ordered Temporal Goals to Most Preferred Policies
Hazhar Rahmani, Abhishek N. Kulkarni, Jie Fu
Markov Decision Process Multi Objective Stochastic Environment Probabilistic Planning Temporal Goal Multi Objective Markov Decision Process

March 22, 2024

Planning with a Learned Policy Basis to Optimally Solve Complex Tasks
Guillermo Infante, David Kuric, Anders Jonsson, Vicenç Gómez, Herke van Hoof
Reinforcement Learning Task Planning Policy Learning Sequential Decision Making Problem Complex Task Stochastic Environment Global Optimality Non Markovian Reward

February 14, 2024

Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path
Qiwei Di, Jiafan He, Dongruo Zhou, Quanquan Gu
Stochastic Environment Minimax Optimal Regret Transition Kernel Stochastic Shortest Path

February 5, 2024

Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays
Qingyuan Wu, Simon Sinong Zhan, Yixuan Wang, Yuhui Wang, Chung-Wei Lin, Chen Lv, Qi Zhu, Jürgen Schmidhuber, Chao Huang
Reinforcement Learning Human Feedback Stochastic Environment State Augmentation Excess Delay

February 1, 2024

Control in Stochastic Environment with Delays: A Model-based Reinforcement Learning Approach
Zhiyuan Yao, Ionut Florescu, Chihoon Lee
External Control Optimal Policy Model Based Reinforcement Learning Policy OpTimization Stochastic Environment Probabilistic Planning Significant Delay Change

January 6, 2024

An Empirical Investigation of Value-Based Multi-objective Reinforcement Learning for Stochastic Environments
Kewen Ding, Peter Vamplew, Cameron Foale, Richard Dazeley
Q Learning Multi Objective Multi Objective Reinforcement Learning Stochastic Environment

December 13, 2023

The Effective Horizon Explains Deep RL Performance in Stochastic Environments
Cassidy Laidlaw, Banghua Zhu, Stuart Russell, Anca Dragan
Reinforcement Learning Deep Reinforcement Learning DQN Agent Stochastic Environment Exploration Algorithm Reinforcement Learning Performance

December 3, 2023

Compositional Policy Learning in Stochastic Control Systems with Formal Guarantees
Đorđe Žikelić, Mathias Lechner, Abhinav Verma, Krishnendu Chatterjee, Thomas A. Henzinger
Reinforcement Learning Stochastic Environment Formal Guarantee Neural Network Policy Reach Avoid Compositional Reinforcement Learning Evolving Policy Stochastic Control System

October 26, 2023

Strategic Abilities of Forgetful Agents in Stochastic Environments
Francesco Belardinelli, Wojciech Jamroga, Munyque Mittelmann, Aniello Murano
Strategy Logic Stochastic Environment Strategic Ability Quantified Uncertainty Memory Less Agent

October 4, 2023

Expected flow networks in stochastic environments and two-player zero-sum games
Marco Jiralerspong, Bilun Sun, Danilo Vucetic, Tianyu Zhang, Yoshua Bengio, Gauthier Gidel, Nikolay Malkin
Generative Flow Network Stochastic Environment Two Player Zero Sum Adversarial Environment Flow Network Consistency Flow Generative Adversarial Network

September 28, 2023

Uncertainty-Aware Decision Transformer for Stochastic Driving Environments
Zenan Li, Fan Nie, Qiao Sun, Fang Da, Hang Zhao
Offline Reinforcement Learning Decision Transformer Stochastic Environment

September 25, 2023

Hierarchical Imitation Learning for Stochastic Environments
Maximilian Igl, Punit Shah, Paul Mougin, Sirish Srinivasan, Tarun Gupta, Brandyn White, Kyriacos Shiarlis, Shimon Whiteson
Imitation Learning Stochastic Environment Behavior Model Conditional Shift

Stochastic Environment

Papers

Model-Based Reward Shaping for Adversarial Inverse Reinforcement Learning in Stochastic Environments

Offline and Distributional Reinforcement Learning for Radio Resource Management

A Survey of Inverse Constrained Reinforcement Learning: Definitions, Progress and Challenges

Certifiably Robust Policies for Uncertain Parametric Environments

Multifidelity digital twin for real-time monitoring of structural dynamics in aquaculture net cages

Recursive Backwards Q-Learning in Deterministic Environments

A Note on Loss Functions and Error Compounding in Model-based Reinforcement Learning

Learning a Formally Verified Control Barrier Function in Stochastic Environment

Preference-Based Planning in Stochastic Environments: From Partially-Ordered Temporal Goals to Most Preferred Policies

Planning with a Learned Policy Basis to Optimally Solve Complex Tasks

Nearly Minimax Optimal Regret for Learning Linear Mixture Stochastic Shortest Path

Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays

Control in Stochastic Environment with Delays: A Model-based Reinforcement Learning Approach

An Empirical Investigation of Value-Based Multi-objective Reinforcement Learning for Stochastic Environments

The Effective Horizon Explains Deep RL Performance in Stochastic Environments

Compositional Policy Learning in Stochastic Control Systems with Formal Guarantees

Strategic Abilities of Forgetful Agents in Stochastic Environments

Expected flow networks in stochastic environments and two-player zero-sum games

Uncertainty-Aware Decision Transformer for Stochastic Driving Environments

Hierarchical Imitation Learning for Stochastic Environments