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Dynamic Weight Adjusting Deep Q-Networks for Real-Time Environmental Adaptation

Xinhao Zhang, Jinghan Zhang, Wujun Si, Kunpeng Liu

TL;DR

This study designs a novel Interactive Dynamic Evaluation Method (IDEM) for DQN that successfully navigates dynamic environments by prioritizing significant transitions based on environmental feedback and learning progress and indicates that under circumstances requiring rapid adaptation, IDEM-DQN can more effectively generalize and stabilize learning.

Abstract

Deep Reinforcement Learning has shown excellent performance in generating efficient solutions for complex tasks. However, its efficacy is often limited by static training modes and heavy reliance on vast data from stable environments. To address these shortcomings, this study explores integrating dynamic weight adjustments into Deep Q-Networks (DQN) to enhance their adaptability. We implement these adjustments by modifying the sampling probabilities in the experience replay to make the model focus more on pivotal transitions as indicated by real-time environmental feedback and performance metrics. We design a novel Interactive Dynamic Evaluation Method (IDEM) for DQN that successfully navigates dynamic environments by prioritizing significant transitions based on environmental feedback and learning progress. Additionally, when faced with rapid changes in environmental conditions, IDEM-DQN shows improved performance compared to baseline methods. Our results indicate that under circumstances requiring rapid adaptation, IDEM-DQN can more effectively generalize and stabilize learning. Extensive experiments across various settings confirm that IDEM-DQN outperforms standard DQN models, particularly in environments characterized by frequent and unpredictable changes.

Dynamic Weight Adjusting Deep Q-Networks for Real-Time Environmental Adaptation

TL;DR

This study designs a novel Interactive Dynamic Evaluation Method (IDEM) for DQN that successfully navigates dynamic environments by prioritizing significant transitions based on environmental feedback and learning progress and indicates that under circumstances requiring rapid adaptation, IDEM-DQN can more effectively generalize and stabilize learning.

Abstract

Deep Reinforcement Learning has shown excellent performance in generating efficient solutions for complex tasks. However, its efficacy is often limited by static training modes and heavy reliance on vast data from stable environments. To address these shortcomings, this study explores integrating dynamic weight adjustments into Deep Q-Networks (DQN) to enhance their adaptability. We implement these adjustments by modifying the sampling probabilities in the experience replay to make the model focus more on pivotal transitions as indicated by real-time environmental feedback and performance metrics. We design a novel Interactive Dynamic Evaluation Method (IDEM) for DQN that successfully navigates dynamic environments by prioritizing significant transitions based on environmental feedback and learning progress. Additionally, when faced with rapid changes in environmental conditions, IDEM-DQN shows improved performance compared to baseline methods. Our results indicate that under circumstances requiring rapid adaptation, IDEM-DQN can more effectively generalize and stabilize learning. Extensive experiments across various settings confirm that IDEM-DQN outperforms standard DQN models, particularly in environments characterized by frequent and unpredictable changes.

Paper Structure

This paper contains 19 sections, 7 equations, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work and Preliminary
  3. Methodology
  4. Methodology Overview
  5. Dynamic Weight Adjustment Mechanism
  6. Temporal Difference Error Calculation
  7. Weight Adjustment Formula
  8. Sampling Mechanism Based on Updated Weights
  9. Adaptive Learning Rate Adjustment Function
  10. Adaptive Learning Rate Adjustment Function
  11. Continuity and Differentiability in Backpropagation
  12. Experiments
  13. Experimental Settings
  14. Environment
  15. Inplemental Details
  16. ...and 4 more sections

Figures (4)

  • Figure 1: Flowchart of IDEM-DQN. Here the IDEM focuses on assigning weights to transitions based on their significance and adjusting learning rates based on real-time error metrics. These enhancements prioritize crucial learning opportunities and optimize the model's response to changing environmental conditions.
  • Figure 2: Loss comparison of DQN and IDEM-DQN on 4x4 FrozenLake grid.
  • Figure 3: DQN and IDEM-DQN performance in 8x8 FrozenLake grid.
  • Figure 4: Ablation study of the learning rate.