Comparing the Efficacy of Fine-Tuning and Meta-Learning for Few-Shot
  Policy Imitation
Massimiliano Patacchiola Mingfei Sun Katja Hofmann Richard E. Turner
In this paper we explore few-shot imitation learning for control problems,
which involves learning to imitate a target policy by accessing a limited set
of offline rollouts. This setting has been relatively under-explored despite
its relevance to robotics and control applications. State-of-the-art methods
developed to tackle few-shot imitation rely on meta-learning, which is
expensive to train as it requires access to a distribution over tasks (rollouts
from many target policies and variations of the base environment). Given this
limitation we investigate an alternative approach, fine-tuning, a family of
methods that pretrain on a single dataset and then fine-tune on unseen
domain-specific data. Recent work has shown that fine-tuners outperform
meta-learners in few-shot image classification tasks, especially when the data
is out-of-domain. Here we evaluate to what extent this is true for control
problems, proposing a simple yet effective baseline which relies on two stages:
(i) training a base policy online via reinforcement learning (e.g. Soft
Actor-Critic) on a single base environment, (ii) fine-tuning the base policy
via behavioral cloning on a few offline rollouts of the target policy. Despite
its simplicity this baseline is competitive with meta-learning methods on a
variety of conditions and is able to imitate target policies trained on unseen
variations of the original environment. Importantly, the proposed approach is
practical and easy to implement, as it does not need any complex meta-training
protocol. As a further contribution, we release an open source dataset called
iMuJoCo (iMitation MuJoCo) consisting of 154 variants of popular OpenAI-Gym
MuJoCo environments with associated pretrained target policies and rollouts,
which can be used by the community to study few-shot imitation learning and
offline reinforcement learning.