mlsanityData to 14 Oct 2026

Decision Theory-Guided Deep Reinforcement Learning for Fast Learning

arXiv 2402.06023 ↗First posted 8 Feb 2024cs.LG cs.AI cs.GTcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

This paper introduces a novel approach, Decision Theory-guided Deep Reinforcement Learning (DT-guided DRL), to address the inherent cold start problem in DRL. By integrating decision theory principles, DT-guided DRL enhances agents' initial performance and robustness in complex environments, enabling more efficient and reliable convergence during learning. Our investigation encompasses two primary problem contexts: the cart pole and maze navigation challenges. Experimental results demonstrate that the integration of decision theory not only facilitates effective initial guidance for DRL agents but also promotes a more structured and informed exploration strategy, particularly in environments characterized by large and intricate state spaces. The results of experiment demonstrate that DT-guided DRL can provide significantly higher rewards compared to regular DRL. Specifically, during the initial phase of training, the DT-guided DRL yields up to an 184% increase in accumulated reward. Moreover, even after reaching convergence, it maintains a superior performance, ending with up to 53% more reward than standard DRL in large maze problems. DT-guided DRL represents an advancement in mitigating a fundamental challenge of DRL by leveraging functions informed by human (designer) knowledge, setting a foundation for further research in this promising interdisciplinary domain.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods. Builds on 8 of its 31 references that are in the map. First posted on arXiv 8 Feb 2024.

Builds on

8 of 31 references.

References · 8

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2016OpenAI Gymcited by 1,736
  3. 2019Challenges of Real-World Reinforcement Learningcited by 291
  4. 2020Transfer Learning in Deep Reinforcement Learning: A Surveycited by 265
  5. 2016A Deep Hierarchical Approach to Lifelong Learning in Minecraftcited by 90
  6. 2016DeepMind Labcited by 74
  7. 2020Learning Time Reduction Using Warm Start Methods for a Reinforcement Learning Based Supervisory Control in Hybrid Electric Vehicle Applicationscited by 3
  8. 2020Tracking the Race Between Deep Reinforcement Learning and Imitation Learning -- Extended Versioncited by 1

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  1. 2024Efficient Exploration in Deep Reinforcement Learning: A Novel Bayesian Actor-Critic Algorithm
  2. 2025A Practical Introduction to Deep Reinforcement Learning
  3. 2025Hybrid Motion Planning with Deep Reinforcement Learning for Mobile Robot Navigation
  4. 2025The Emergence of Deep Reinforcement Learning for Path Planning
  5. 2024Large Language Model guided Deep Reinforcement Learning for Decision Making in Autonomous Driving
  6. 2024Demystifying the Physics of Deep Reinforcement Learning-Based Autonomous Vehicle Decision-Making
  7. 2024Deep Reinforcement Learning for Mobile Robot Path Planning
  8. 2020Exploration-efficient Deep Reinforcement Learning with Demonstration Guidance for Robot Control