mlsanityData to 14 Oct 2026

A Comparison of Reinforcement Learning and Optimal Control Methods for Path Planning

arXiv 2604.12628 ↗First posted 14 Apr 2026math.OC cs.ROcited by 1

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

Abstract (arXiv)

Path-planning for autonomous vehicles in threat-laden environments is a fundamental challenge. While traditional optimal control methods can find ideal paths, the computational time is often too slow for real-time decision-making. To solve this challenge, we propose a method based on Deep Deterministic Policy Gradient (DDPG) and model the threat as a simple, circular `no-go' zone. A mission failure is claimed if the vehicle enters this `no-go' zone at any time or does not reach a neighborhood of the destination. The DDPG agent is trained to learn a direct mapping from its current state (position and velocity) to a series of feasible actions that guide the agent to safely reach its goal. A reward function and two neural networks, critic and actor, are used to describe the environment and guide the control efforts. The DDPG trains the agent to find the largest possible set of starting points (``feasible set'') wherein a safe path to the goal is guaranteed. This provides critical information for mission planning, showing beforehand whether a task is achievable from a given starting point, assisting pre-mission planning activities. The approach is validated in simulation. A comparison between the DDPG method and a traditional optimal control (pseudo-spectral) method is carried out. The results show that the learning-based agent may produce effective paths while being significantly faster, making it a better fit for real-time applications. However, there are areas (``infeasible set'') where the DDPG agent cannot find paths to the destination, and the paths in the feasible set may not be optimal. These preliminary results guide our future research: (1) improve the reward function to enlarge the DDPG feasible set, (2) examine the feasible set obtained by the pseudo-spectral method, and (3) investigate the arc-search IPM method for the path planning problem.

Key facts. Cited by 1 paper in the map (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 5 of its 34 references that are in the map. First posted on arXiv 14 Apr 2026.

Builds on

5 of 34 references.

References · 5

  1. 2015Continuous control with deep reinforcement learningcited by 3,407
  2. 2023UAV Path Planning Employing MPC- Reinforcement Learning Method Considering Collision Avoidancecited by 7
  3. 2023Q-Learning based system for path planning with unmanned aerial vehicles swarms in obstacle environmentscited by 4
  4. 2021An Optimal Engagement Zone Avoidance Scenario in 2-Dcited by 3
  5. 2023Learning-Based UAV Path Planning for Data Collection with Integrated Collision Avoidancecited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Path Planning Using Deep Deterministic Policy Gradient: A Reinforcement Learning Approachcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025The Emergence of Deep Reinforcement Learning for Path Planning
  2. 2024Autonomous Navigation of Unmanned Vehicle Through Deep Reinforcement Learning
  3. 2022Planning and Learning: Path-Planning for Autonomous Vehicles, a Review of the Literature
  4. 2024Deep Reinforcement Learning in Autonomous Car Path Planning and Control: A Survey
  5. 2024Deep Reinforcement Learning for Mobile Robot Path Planning
  6. 2021A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles
  7. 2026Combining Reinforcement Learning with Arc-search Interior-Point Method for Path Planning
  8. 2026Comprehensive Comparisons of Reinforcement Learning Algorithms in Autonomous Driving