\section{Introduction}
Deep reinforcement learning (DRL) has been an active research area in recent years, with significant progress in developing algorithms that can learn to play complex games at superhuman levels. One of the most notable achievements in this area is the DRL algorithm proposed by Mnih et al. \citep{mnih2013playing}, which achieved state-of-the-art performance on a suite of Atari 2600 games. This algorithm, called Deep Q-Network (DQN), combines deep neural networks with reinforcement learning to learn a policy that maximizes the expected cumulative reward. 

The success of DQN has motivated further research in DRL, with the aim of improving its performance and extending its applicability to other domains. One of the key challenges in DRL is the trade-off between exploration and exploitation, which is particularly important in domains with large state and action spaces. This challenge has been addressed in various ways, such as by using different exploration strategies \citep{mnih2016asynchronous}, incorporating prior knowledge \citep{haarnoja2018soft}, and using different architectures \citep{lillicrap2015continuous}. 

In this paper, we propose a novel DRL algorithm that combines several recent advances in the field. Our algorithm, called Rainbow, builds on the DQN architecture and incorporates six extensions that have been shown to improve its performance. These extensions include prioritized experience replay \citep{schaul2015prioritized}, dueling network architecture \citep{wang2015dueling}, multi-step learning \citep{mnih2016asynchronous}, distributional reinforcement learning \citep{bellemare2017distributional}, noisy networks \citep{fortunato2017noisy}, and a new hyperparameter tuning method called hyperparameter optimization via probabilistic modeling (HOPM) \citep{falkner2018bohb}. 

The main research question we address in this paper is whether Rainbow can achieve better performance than DQN and other state-of-the-art DRL algorithms on a suite of Atari 2600 games. To answer this question, we conduct experiments on a set of 57 Atari games and compare the performance of Rainbow with that of DQN and several other algorithms. Our results show that Rainbow outperforms all other algorithms on average and achieves state-of-the-art performance on 43 out of 57 games. 

The contributions of this paper are threefold. First, we propose a novel DRL algorithm that combines six recent extensions to the DQN architecture. Second, we conduct extensive experiments on a large set of Atari 2600 games to evaluate the performance of Rainbow and compare it with that of other state-of-the-art DRL algorithms. Third, we introduce a new hyperparameter tuning method, HOPM, which is shown to be more efficient than existing methods in finding good hyperparameter settings. 

Related works in the field of DRL include prior work on DQN \citep{mnih2013playing}, as well as subsequent extensions to the algorithm, such as double Q-learning \citep{van2016deep}, dueling network architecture \citep{wang2015dueling}, and prioritized experience replay \citep{schaul2015prioritized}. Rainbow builds on these extensions and incorporates several new ones, such as distributional reinforcement learning \citep{bellemare2017distributional}, noisy networks \citep{fortunato2017noisy}, multi-step learning \citep{mnih2016asynchronous}, and HOPM \citep{falkner2018bohb}. 

In summary, this paper proposes a novel DRL algorithm called Rainbow, which combines six recent extensions to the DQN architecture. We conduct extensive experiments to evaluate the performance of Rainbow on a suite of Atari 2600 games and compare it with that of other state-of-the-art DRL algorithms. Our results show that Rainbow achieves state-of-the-art performance on a majority of the games and outperforms all other algorithms on average.