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Autonomous Goal Detection and Cessation in Reinforcement Learning: A Case Study on Source Term Estimation

Yiwei Shi, Muning Wen, Qi Zhang, Weinan Zhang, Cunjia Liu, Weiru Liu

TL;DR

This work tackles autonomous goal detection and cessation in reinforcement learning under sparse environmental feedback, using Source Term Estimation as a rigorous testbed. It introduces AGDC, a self-feedback module that blends Bayesian belief estimation via a particle filter with a simple cessation criterion expressed as $STD = sqrt(diag(Cov(Theta)))$ and a threshold $\zeta$, enabling agents to stop upon converging beliefs. The authors validate AGDC by integrating it with DQN, PPO, and DDPG and comparing against Infotaxis, Entrotaxis, DCEE, and a random baseline, reporting improvements in success rate, traveled distance, and search time. The results demonstrate that AGDC-enhanced RL methods maintain robustness in uncertain, feedback-limited STE environments and point to practical impact for environmental monitoring and emergency response.

Abstract

Reinforcement Learning has revolutionized decision-making processes in dynamic environments, yet it often struggles with autonomously detecting and achieving goals without clear feedback signals. For example, in a Source Term Estimation problem, the lack of precise environmental information makes it challenging to provide clear feedback signals and to define and evaluate how the source's location is determined. To address this challenge, the Autonomous Goal Detection and Cessation (AGDC) module was developed, enhancing various RL algorithms by incorporating a self-feedback mechanism for autonomous goal detection and cessation upon task completion. Our method effectively identifies and ceases undefined goals by approximating the agent's belief, significantly enhancing the capabilities of RL algorithms in environments with limited feedback. To validate effectiveness of our approach, we integrated AGDC with deep Q-Network, proximal policy optimization, and deep deterministic policy gradient algorithms, and evaluated its performance on the Source Term Estimation problem. The experimental results showed that AGDC-enhanced RL algorithms significantly outperformed traditional statistical methods such as infotaxis, entrotaxis, and dual control for exploitation and exploration, as well as a non-statistical random action selection method. These improvements were evident in terms of success rate, mean traveled distance, and search time, highlighting AGDC's effectiveness and efficiency in complex, real-world scenarios.

Autonomous Goal Detection and Cessation in Reinforcement Learning: A Case Study on Source Term Estimation

TL;DR

This work tackles autonomous goal detection and cessation in reinforcement learning under sparse environmental feedback, using Source Term Estimation as a rigorous testbed. It introduces AGDC, a self-feedback module that blends Bayesian belief estimation via a particle filter with a simple cessation criterion expressed as and a threshold , enabling agents to stop upon converging beliefs. The authors validate AGDC by integrating it with DQN, PPO, and DDPG and comparing against Infotaxis, Entrotaxis, DCEE, and a random baseline, reporting improvements in success rate, traveled distance, and search time. The results demonstrate that AGDC-enhanced RL methods maintain robustness in uncertain, feedback-limited STE environments and point to practical impact for environmental monitoring and emergency response.

Abstract

Reinforcement Learning has revolutionized decision-making processes in dynamic environments, yet it often struggles with autonomously detecting and achieving goals without clear feedback signals. For example, in a Source Term Estimation problem, the lack of precise environmental information makes it challenging to provide clear feedback signals and to define and evaluate how the source's location is determined. To address this challenge, the Autonomous Goal Detection and Cessation (AGDC) module was developed, enhancing various RL algorithms by incorporating a self-feedback mechanism for autonomous goal detection and cessation upon task completion. Our method effectively identifies and ceases undefined goals by approximating the agent's belief, significantly enhancing the capabilities of RL algorithms in environments with limited feedback. To validate effectiveness of our approach, we integrated AGDC with deep Q-Network, proximal policy optimization, and deep deterministic policy gradient algorithms, and evaluated its performance on the Source Term Estimation problem. The experimental results showed that AGDC-enhanced RL algorithms significantly outperformed traditional statistical methods such as infotaxis, entrotaxis, and dual control for exploitation and exploration, as well as a non-statistical random action selection method. These improvements were evident in terms of success rate, mean traveled distance, and search time, highlighting AGDC's effectiveness and efficiency in complex, real-world scenarios.
Paper Structure (24 sections, 17 equations, 6 figures, 2 tables, 4 algorithms)

This paper contains 24 sections, 17 equations, 6 figures, 2 tables, 4 algorithms.

Table of Contents

  1. Introduction
  2. Problem Formulation and Peliminaries
  3. Gaseous Diffusion Model
  4. Sensor Models
  5. Partially Observable Markov Decision Process
  6. Methodology
  7. Bayesian Approximation for Belief Distribution
  8. Autonomous Goal Detection and Cessation
  9. Experiment
  10. Baseline Algorithms and Evaluation Metrics
  11. Scenario Parameterization and Evaluation
  12. Particle Numbers and Cessation Thresholds
  13. Further Research Experiments
  14. Conclusion
  15. Appendix
  16. ...and 9 more sections

Figures (6)

  • Figure 1: AGDC Structure Diagram for Solving the STE
  • Figure 2: The example of map of the Gas-Diffusion model
  • Figure 3: Comparative Analysis of Particle Numbers (PN) and Cessation Thresholds(CT)
  • Figure 4: Trajectories of Various Methods
  • Figure 5: Multiple Trajectories from True Trajectory and Estimated (Belief) Trajectory with Color Gradient
  • ...and 1 more figures