Fast and Data Efficient Reinforcement Learning from Pixels via Non-Parametric Value Approximation
This addresses the challenge of slow and data-hungry reinforcement learning for researchers and practitioners in AI, though it is incremental as it builds on existing lazy-learning and episodic methods.
The paper tackled the problem of sample and computation inefficiency in pixel-based reinforcement learning by introducing NAIT, a non-parametric algorithm that achieved competitive performance on ATARI100k benchmarks with over 100x speedup in wall-time.
We present Nonparametric Approximation of Inter-Trace returns (NAIT), a Reinforcement Learning algorithm for discrete action, pixel-based environments that is both highly sample and computation efficient. NAIT is a lazy-learning approach with an update that is equivalent to episodic Monte-Carlo on episode completion, but that allows the stable incorporation of rewards while an episode is ongoing. We make use of a fixed domain-agnostic representation, simple distance based exploration and a proximity graph-based lookup to facilitate extremely fast execution. We empirically evaluate NAIT on both the 26 and 57 game variants of ATARI100k where, despite its simplicity, it achieves competitive performance in the online setting with greater than 100x speedup in wall-time.