The deep reinforcement learning community has made several independent
improvements to the DQN algorithm. However, it is unclear which of these
extensions are complementary and can be fruitfully combined. This paper
examines six extensions to the DQN algorithm and empirically studies their
combination. Our experiments show that the combination provides
state-of-the-art performance on the Atari 2600 benchmark, both in terms of data
efficiency and final performance. We also provide results from a detailed
ablation study that shows the contribution of each component to overall
performance.Comment: Under review as a conference paper at AAAI 201
