Towards General-Purpose Model-Free Reinforcement Learning
Authors: name: Scott Fujimoto, name: Pierluca D'Oro, name: Amy Zhang, name: Yuandong Tian, name: Michael Rabbat

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.