Reinforcement learning with world model
  Nowadays, model-free reinforcement learning algorithms have achieved
remarkable performance on many decision making and control tasks, but high
sample complexity and low sample efficiency still hinder the wide use of
model-free reinforcement learning algorithms. In this paper, we argue that if
we intend to design an intelligent agent that learns fast and transfers well,
the agent must be able to reflect key elements of intelligence, like intuition,
Memory, PredictionandCuriosity. We propose an agent framework that integrates
off-policy reinforcement learning with world model learning, so as to embody
the important features of intelligence in our algorithm design. We adopt the
state-of-art model-free reinforcement learning algorithm, Soft Actor-Critic, as
the agent intuition, and world model learning through RNN to endow the agent
with memory, curiosity, and the ability to predict. We show that these ideas
can work collaboratively with each other and our agent (RMC) can give new
state-of-art results while maintaining sample efficiency and training
stability. Moreover, our agent framework can be easily extended from MDP to
POMDP problems without performance loss.
