Reinforcement Learning Techniques for Mobile Robot Path Planning

Reinforcement learning (RL) has emerged as a pivotal approach for enabling mobile robots to autonomously discover collision-free trajectories through repeated interaction with their environment. In this paradigm, an agent observes its state—typically derived from sensory inputs such as LIDAR scans or camera feeds—selects an action from a predefined set, and receives a scalar reward that reflects progress towards a goal or penalty for undesired events such as collisions. Traditional value-based algorithms, notably Q-learning and its on-policy counterpart SARSA, learn action-value tables that are well suited to small, discrete state spaces but suffer from slow convergence and large memory demands. Model-based extensions such as Dyna-Q incorporate virtual planning steps to accelerate learning, while deep reinforcement learning replaces tables with neural function approximators, enabling policies to scale to high-dimensional observations. Policy-gradient and actor-critic methods, including the soft actor-critic framework, introduce stochastic policies with entropy maximisation to encourage exploration. Recent innovations combine hindsight experience replay to reuse unsuccessful trials, heuristic graph guidance to shrink search spaces, and adaptive exploration-exploitation schedules to improve sample efficiency. Collectively, these techniques have delivered robust performance in grid maps, topological representations and continuous environments, finding applications in warehouse automation, search-and-rescue missions and dynamic indoor navigation.

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Recent advances have refined the soft actor-critic algorithm for path planning by integrating a maximum-entropy objective with hindsight experience replay. This enhanced approach maintains a balance between exploration and exploitation, reuses past failures as additional positive samples and demonstrates superior convergence rates in complex simulated environments.

Improvements to the classical Dyna-Q framework have been achieved through the introduction of a heuristic graph for global path guidance and a novel dynamic reward function. By reducing the effective search space and intensifying feedback in sparse-reward settings, this method attains faster convergence and more reliable obstacle avoidance in dynamic environments populated by moving obstacles.

A hybrid Q-learning algorithm augmented by a paired whale optimisation scheme accelerates convergence in large or cluttered maps. Initial Q-values are set via the whale optimiser, selective exploration strategies bias action selection towards the goal, and a nonlinear decay of the ε-greedy parameter balances early exploration with later exploitation. Empirical results highlight notable improvements in path optimality and learning speed over standard Q-learning.

Reinforcement Learning Techniques for Mobile Robot Path Planning publication trend

The graph below shows the total number of articles in reinforcement learning techniques for mobile robot path planning across all publications each year (not limited to Nature Index journals).

Technical terms

Reinforcement learning: A learning paradigm in which an agent maximises cumulative reward by trial and error in an environment.

Q-learning: A value-based RL algorithm that learns the expected reward of taking an action in a given state and following the optimal policy thereafter.

Deep reinforcement learning: An RL approach that employs deep neural networks to approximate value functions or policies, handling high-dimensional inputs.

Soft actor-critic (SAC): An off-policy actor-critic method that incorporates an entropy maximisation term to encourage policy robustness and exploration.

Experience replay: A mechanism where past transitions are stored and repeatedly sampled to break correlation in updates and improve sample efficiency.

Exploration-exploitation trade-off: The balance between choosing known rewarding actions (exploitation) and trying novel actions to discover potentially better rewards (exploration).

References

1. Improved Dyna-Q: A Reinforcement Learning Method Focused via Heuristic Graph for AGV Path Planning in Dynamic Environments. Drones (2022).
2. A Path-Planning Method Based on Improved Soft Actor-Critic Algorithm for Mobile Robots. Biomimetics (2023).
3. A novel Q-learning algorithm based on improved whale optimization algorithm for path planning. PLOS ONE (2022).