Towards General-Purpose Model-Free Reinforcement Learning (2501.16142v1)

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.

• The paper presents MR.Q, which unifies model-free and model-based reinforcement learning by leveraging dynamics-based representations for consistent performance.
• It employs a multi-step learning strategy and a unified embedding space to operate effectively across varied environments like Gym, DMC, and Atari with a single configuration.
• Empirical results show MR.Q achieves competitive state-of-the-art scores with fewer parameters and faster training, marking a significant advance toward general-purpose RL.

The paper "Towards General-Purpose Model-Free Reinforcement Learning" investigates the development of a general model-free deep reinforcement learning (RL) algorithm named MR.Q, which seeks to unify model-free and model-based advantages. The primary motivation is to address the highly specialized and benchmark-dependent nature of current RL algorithms by developing an approach capable of performing consistently across diverse environments without hyperparameter tuning.

Algorithm Design and Features:

• Model-Free yet Leverages Model-Based Representations: MR.Q attempts to bridge the gap between model-free and model-based RL by leveraging dynamics-based representations to effectively approximate a linear relationship with the true value function. This approach is intended to harness the benefits of model-based representations—which often provide rich information about the environment's dynamics—while avoiding the computational expenses of full model-based planning.
• Optimization Strategy: MR.Q utilizes a representation learning strategy wherein state-action pairs are mapped into embeddings that share a linear relationship with the expected returns. The algorithm incorporates a multi-step learning approach and categorical reward loss to ensure robust performance across tasks with varying reward structures.
• Embedded and Unified Learning Architecture: The paper introduces a unified embedding space that abstracts the problem domain, decoupling particular input space characteristics such as vector versus pixel observations. This allows MR.Q to operate with a standardized set of parameters, marking a departure from current methods that require distinct configurations for discrete versus continuous spaces.

Empirical Evaluation:

MR.Q was evaluated on four popular RL benchmarks, including Gym locomotion tasks, DeepMind Control Suite (DMC) both with proprioceptive and visual observations, and Atari. In each case, MR.Q used a single configuration across diverse environments.

1. Performance Across Benchmarks:
   • MR.Q demonstrated competitive performance against domain-specific and general-purpose baselines.
   • In the DMC benchmark, both proprioceptive and visual tasks, MR.Q outperformed other tested methods, showcasing the versatility and robustness of its approach.
   • While slightly less performant than TD7 in the Gym locomotion suite, MR.Q maintained state-of-the-art comparable scores.
   • In Atari, it showed better results than other model-free algorithms simulating discrete action environments, though DreamerV3 demonstrated higher performance with significantly larger models.
2. Efficient Use of Resources:
   • MR.Q achieved this breadth of applicability with a computationally lightweight architecture, fewer network parameters, and faster training and evaluation speeds compared to comprehensive model-based methods like DreamerV3.

Design Study and Insights:

• The paper undertaken presents insights into various design decisions contributing to MR.Q's robust performance. Specifically, it highlights the role of leveraging model-based loss for representation learning, multi-step returns, and reward scaling.
• Key design elements and hyperparameter settings were shown to influence outcomes variably across benchmarks, highlighting the necessity of multiscale evaluations for genuinely general-purpose methods.

Conclusion and Implications:

The work posits MR.Q as an advancement towards general-purpose RL by suggesting that model-based representations can offer significant value in model-free contexts. It indicates a potential paradigm shift where robust, generalized learning may be achieved through strategic representation learning without the necessity of full model-based approaches.

Overall, MR.Q represents a meaningful step toward creating efficient and effective RL agents capable of operating across a wide range of environments with minimal dependence on environment-specific tuning. The findings suggest future work should explore more complex environments to fully delineate the capabilities and limits of model-free methods augmented with model-based insights.