  Many reinforcement learning (RL) tasks have specific properties that can be
leveraged to modify existing RL algorithms to adapt to those tasks and further
improve performance, and a general class of such properties is the multiple
reward channel. In those environments the full reward can be decomposed into
sub-rewards obtained from different channels. Existing work on reward
decomposition either requires prior knowledge of the environment to decompose
the full reward, or decomposes reward without prior knowledge but with degraded
performance. In this paper, we propose Distributional Reward Decomposition for
Reinforcement Learning (DRDRL), a novel reward decomposition algorithm which
captures the multiple reward channel structure under distributional setting.
Empirically, our method captures the multi-channel structure and discovers
meaningful reward decomposition, without any requirements on prior knowledge.
Consequently, our agent achieves better performance than existing methods on
environments with multiple reward channels.
