Hanabi has become a popular game for research when it comes to reinforcement learning (RL) as it is one of the few cooperative card games where you have incomplete knowledge of the entire environment, thus presenting a challenge for a RL agent. We explored different tabular and deep reinforcement learning algorithms to see which had the best performance both against an agent of the same type and also against other types of agents. We establish that certain agents played their highest scoring games against specific agents while others exhibited higher scores on average by adapting to the opposing agent's behavior. We attempted to quantify the conditions under which each algorithm provides the best advantage and identified the most interesting interactions between agents of different types. In the end, we found that temporal difference (TD) algorithms had better overall performance and balancing of play types compared to tabular agents. Specifically, tabular Expected SARSA and deep Q-Learning agents showed the best performance.

Development of artificial intelligence agents tends to frequently be measured by their performance in games, but there's a good reason for that: Games tend to offer a wide proficiency curve, in terms of being relatively simple to grasp the basics, but difficult to master, and they almost always have a built-in scoring system to evaluate performance. DeepMind's agents have tackled board game Go, as well as real-time strategy video game StarCraft. But the Alphabet company's most recent feat is Agent57, a learning agent that can beat the average human on each of 57 Atari games with a wide range of difficulty, characteristics and gameplay styles. Being better than humans at 57 Atari games may seem like an odd benchmark against which to measure the performance of a deep learning agent, but it's actually a standard that goes all the way back to 2012, with a selection of Atari classics including Pitfall, Solaris, Montezuma's Revenge and many others. Taken together, these games represent a broad range of difficulty levels, as well as requiring a range of different strategies in order to achieve success.

The rise of'deep learning' has caused a lot of excitement around the revolutionary capabilities of these artificially intelligent agents. But it's also raised fear and suspicion about what exactly is going on inside each algorithm. One way for us to gain some understanding of our silicon-based friends (or foes?) is for them to disclose their framework of decision-making in a way that we humans can understand – by using the concept of personality. My research explores how some of these deep learning agents can be better understood through their'personalities' – like whether they are'greedy', 'selfish' or'prudent'. We are now at the dawn of a new era in AI technology – a so-called fourth industrial revolution that will reshape every industry.