  Deep Reinforcement Learning (Deep RL) has been in the spotlight for the past
few years, due to its remarkable abilities to solve problems which were
considered to be practically unsolvable using traditional Machine Learning
methods. However, even state-of-the-art Deep RL algorithms have various
weaknesses that prevent them from being used extensively within industry
applications, with one such major weakness being their sample-inefficiency. In
an effort to patch these issues, we integrated a meta-learning technique in
order to shift the objective of learning to solve a task into the objective of
learning how to learn to solve a task (or a set of tasks), which we empirically
show that improves overall stability and performance of Deep RL algorithms. Our
model, named REIN-2, is a meta-learning scheme formulated within the RL
framework, the goal of which is to develop a meta-RL agent (meta-learner) that
learns how to produce other RL agents (inner-learners) that are capable of
solving given environments. For this task, we convert the typical interaction
of an RL agent with the environment into a new, single environment for the
meta-learner to interact with. Compared to traditional state-of-the-art Deep RL
algorithms, experimental results show remarkable performance of our model in
popular OpenAI Gym environments in terms of scoring and sample efficiency,
including the Mountain Car hard-exploration environment.
