Evolving Generalizable Actor-Critic Algorithms. (arXiv:2204.04292v2 [cs.LG] UPDATED)

Deploying Reinforcement Learning (RL) agents in the real world requires
designing and tuning algorithms for problem-specific objectives such as
performance, robustness, or stability. These objectives can frequently change,
which will then necessitate further painstaking design and tuning. This paper
presents MetaPG, an evolutionary method for designing new loss functions for
actor-critic RL algorithms that optimize for different objectives. In
particular, we focus on the objectives of final performance in training regime,
policy robustness to unseen environment configurations, and training curve …

actor-critic algorithms arxiv