Towards General-Purpose Model-Free Reinforcement Learning
Scott Fujimoto, Pierluca D'Oro, Amy Zhang, Yuandong Tian, Michael Rabbat
2025-01-28
Summary
This paper talks about a new approach to reinforcement learning (RL) called MR.Q. It aims to create a single, versatile algorithm that can solve many different types of problems without needing to be adjusted for each specific task.
What's the problem?
Current RL methods often need to be fine-tuned for specific tasks, which limits their usefulness. While some newer methods can handle multiple tasks, they're complex and slow. This makes it hard to use RL for a wide range of real-world problems efficiently.
What's the solution?
The researchers developed MR.Q, a model-free RL algorithm that borrows some ideas from model-based methods. It uses a clever way to represent information about the task, making it easier for the algorithm to learn. This approach allows MR.Q to work well on many different types of problems without needing to change its settings. They tested MR.Q on various common RL challenges and found it performed well compared to other methods, even those designed for specific tasks.
Why it matters?
This research matters because it's a step towards creating more flexible and efficient AI systems. If we can have a single algorithm that can tackle many different problems without needing constant adjustments, it could make AI more practical and useful in real-world situations. This could lead to advances in areas like robotics, game AI, and automated decision-making systems. It also shows that we can combine the best parts of different RL approaches to create something new and potentially more powerful.
Abstract
Reinforcement learning (RL) promises a framework for near-universal problem-solving. In practice however, RL algorithms are often tailored to specific benchmarks, relying on carefully tuned hyperparameters and algorithmic choices. Recently, powerful model-based RL methods have shown impressive general results across benchmarks but come at the cost of increased complexity and slow run times, limiting their broader applicability. In this paper, we attempt to find a unifying model-free deep RL algorithm that can address a diverse class of domains and problem settings. To achieve this, we leverage model-based representations that approximately linearize the value function, taking advantage of the denser task objectives used by model-based RL while avoiding the costs associated with planning or simulated trajectories. We evaluate our algorithm, MR.Q, on a variety of common RL benchmarks with a single set of hyperparameters and show a competitive performance against domain-specific and general baselines, providing a concrete step towards building general-purpose model-free deep RL algorithms.