  Most reinforcement learning (RL) methods only focus on learning a single task
from scratch and are not able to use prior knowledge to learn other tasks more
effectively. Context-based meta RL techniques are recently proposed as a
possible solution to tackle this. However, they are usually less efficient than
conventional RL and may require many trial-and-errors during training. To
address this, we propose a novel meta-RL approach that achieves competitive
performance comparing to existing meta-RL algorithms, while requires
significantly fewer environmental interactions. By combining context variables
with the idea of decomposing reward in successor feature framework, our method
does not only learn high-quality policies for multiple tasks simultaneously but
also can quickly adapt to new tasks with a small amount of training. Compared
with state-of-the-art meta-RL baselines, we empirically show the effectiveness
and data efficiency of our method on several continuous control tasks.

Machine Learning;Artificial Intelligence