Recently, we have seen a rapidly growing adoption of Deep Reinforcement Learning (DRL) technologies. Fully achieving the promise of these technologies in practice is, however, extremely difficult. Users have to invest tremendous efforts in building DRL agents, incorporating the agents into various external training environments, and tuning agent implementation/hyper-parameters so that they can reproduce state-of-the-art (SOTA) performance. In this paper, we propose RLzoo, a new DRL library that aims to make it easy to develop and reproduce DRL algorithms. RLzoo has both high-level APIs and low-level APIs, useful for constructing and customising DRL agents, respectively. It has an adaptive agent construction algorithm that can automatically integrate custom RLzoo agents into various external training environments. To help reproduce the results of SOTA algorithms, RLzoo provides rich reference DRL algorithm implementations and effective hyper-parameter settings. Extensive evaluation results show that RLzoo not only outperforms existing DRL libraries in its simplicity of API design; but also provides the largest number of reference DRL algorithm implementations.

This is the second part of an article discussing new areas of game theory that are influencing deep reinforcement learning systems. The first part focused on types of games that we are actively seeing in multi-agent reinforcement learning systems. Today, I would like to cover three new areas of deep learning theory that can influence new generations of reinforcement learning systems. Game theory plays a fundamental factor in modern artificial intelligence(AI) solutions. Specifically, deep reinforcement learning(DRL) is an area of AI that embraced game theory as a first-class citize.

Game theory plays a fundamental factor in modern artificial intelligence(AI) solutions. Specifically, deep reinforcement learning(DRL) is an area of AI that embraced game theory as a first-class citize. From single-agent programs to complex multi-agent DRL environments, gamifying dynamics are present across the lifecycle of AI programs. The fascinating thing is that the rapid evolution of DRL has also triggered a renewed interesting in game theory research. The relationship between game theory and DRL seems trivial.