In this article, the authors explore the use of reinforcement learning (RL) to improve robotic navigation. They argue that traditional RL approaches often struggle with navigating complex environments, as they prioritize single-objective reward functions that may not align with real-world goals. To address this issue, the authors propose using multi-objective RL, which considers multiple conflicting objectives simultaneously.
The authors begin by explaining the basic components of a Markov decision process (MDP), which is the framework used to structure RL problems. They define an agent (the robot) that interacts with an environment, receiving rewards based on its actions. The environment represents the real-world space, while states and actions are defined in the context of the problem.
To enhance navigation, the authors propose using a reward function that balances between two conflicting objectives: energy efficiency and robot movement. They explain that making decisions under these requirements is inherently a multi-objective problem, as increasing energy efficiency may require slower movement, while faster movement may result in higher energy consumption.
To address this challenge, the authors evaluate single and multi-objective reinforcement learning strategies. Single-objective RL approaches prioritize a single reward function, often resulting in suboptimal navigation. Multi-objective RL, on the other hand, considers multiple conflicting objectives, allowing the agent to balance between energy efficiency and robot movement.
The authors demonstrate their approach using a simulated robotic environment, where they compare the performance of single-objective and multi-objective RL strategies. They find that the multi-objective approach achieves better navigation performance, as it balances between the two conflicting objectives.
Finally, the authors discuss potential applications of their approach in real-world environments, such as warehouse robots or autonomous vehicles. They argue that by prioritizing energy efficiency and movement balance, these robots can navigate complex spaces more efficiently and safely.
In summary, this article proposes using multi-objective reinforcement learning to improve robotic navigation in complex environments. By balancing between energy efficiency and robot movement, the proposed approach achieves better navigation performance than traditional single-objective RL strategies. The authors demonstrate their approach through simulations and highlight potential applications in real-world settings.
Enhancing Robotic Navigation via Multi-Objective Reinforcement Learning Strategies
