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

arXiv: https://arxiv.org/abs/2604.12628 · HTML: https://mlsanity.com/paper/2604.12628/

## Key facts

- Cited by 1 papers 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.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 5 of 5)

- [Continuous control with deep reinforcement learning](https://mlsanity.com/paper/1509.02971/) (2015)
- [UAV Path Planning Employing MPC- Reinforcement Learning Method Considering Collision Avoidance](https://mlsanity.com/paper/2302.10669/) (2023)
- [Q-Learning based system for path planning with unmanned aerial vehicles swarms in obstacle environments](https://mlsanity.com/paper/2303.17655/) (2023)
- [An Optimal Engagement Zone Avoidance Scenario in 2-D](https://mlsanity.com/paper/2111.05904/) (2021)
- [Learning-Based UAV Path Planning for Data Collection with Integrated Collision Avoidance](https://mlsanity.com/paper/2312.06225/) (2023)

## Built on by (most cited first, 1 of 1)

- [Path Planning Using Deep Deterministic Policy Gradient: A Reinforcement Learning Approach](https://mlsanity.com/paper/2606.07855/) (2026)

## 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.