Towards General-Purpose Model-Free Reinforcement Learning

Understand

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.