DoMo-AC: Doubly Multi-step Off-policy Actor-Critic Algorithm
This addresses a fundamental bottleneck in reinforcement learning for practitioners, though it appears incremental as it builds on existing architectures like IMPALA.
The paper tackled the limited impact of multi-step learning in optimal control by introducing DoMo-AC, a practical algorithm that combines multi-step policy improvements and evaluations, showing improvements over baseline methods on Atari-57 benchmarks.
Multi-step learning applies lookahead over multiple time steps and has proved valuable in policy evaluation settings. However, in the optimal control case, the impact of multi-step learning has been relatively limited despite a number of prior efforts. Fundamentally, this might be because multi-step policy improvements require operations that cannot be approximated by stochastic samples, hence hindering the widespread adoption of such methods in practice. To address such limitations, we introduce doubly multi-step off-policy VI (DoMo-VI), a novel oracle algorithm that combines multi-step policy improvements and policy evaluations. DoMo-VI enjoys guaranteed convergence speed-up to the optimal policy and is applicable in general off-policy learning settings. We then propose doubly multi-step off-policy actor-critic (DoMo-AC), a practical instantiation of the DoMo-VI algorithm. DoMo-AC introduces a bias-variance trade-off that ensures improved policy gradient estimates. When combined with the IMPALA architecture, DoMo-AC has showed improvements over the baseline algorithm on Atari-57 game benchmarks.