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Title: Hierarchical Deep Reinforcement Learning Agent with Counter Self-play  on Competitive Games 

Paper content:
Deep Reinforcement Learning algorithms lead to agents that can solve difficult decision making problems in complex environments.
However, many difficult multi-agent competitive games, especially real-time strategy games are still considered beyond the capability of current deep reinforcement learning algorithms, although there has been a recent effort to change this \citep{openai_2017_dota, vinyals_2017_starcraft}.
Moreover, when the opponents in a competitive game are suboptimal, the current \textit{Nash Equilibrium} seeking, self-play algorithms are often unable to generalize their strategies to opponents that play strategies vastly different from their own.
This suggests that a learning algorithm that is beyond conventional self-play is necessary.
We develop Hierarchical Agent with Self-play (HASP), a learning approach for obtaining hierarchically structured policies that can achieve higher performance than conventional self-play on competitive games through the use of a diverse pool of sub-policies we get from Counter Self-Play (CSP).
We demonstrate that the ensemble policy generated by HASP can achieve better performance while facing unseen opponents that use sub-optimal policies.
On a motivating iterated Rock-Paper-Scissor game and a partially observable real-time strategic game (http://generals.io/), we are led to the conclusion that HASP can perform better than conventional self-play as well as achieve 77% win rate against FloBot, an open-source agent which has ranked at position number 2 on the online leaderboards.
Deep reinforcement learning (RL) has achieved significant success on many complex sequential decision-making problems.
Most of the problems are in robotics domain or video games BID16 .
However, complex real-time strategic competitive games still pose a strong challenge to the current deep reinforcement learning method due to the requirement of the ability to handle long-term scheduling, partial observability and multi-agent collaboration/competition.
BID28 BID20 .
Competitive games such as Go, in which each player optimize their own interests by finding the best response to opponents' strategies, are usually studied mainly on finding the Nash Equilibrium solutions BID20 , namely a combination of players' strategies upon which neither player can obtain higher rewards by modifying their strategy unilaterally BID23 .
However, in the real-world, opponents can have a variety of strengths and play styles and do not always adopt the equilibrium solutions.
In fact, human players are often remarkably good at analyzing strategies, tendencies, and flaws in opponents' behavior and then exploiting the opponents even if the resulting exploiting strategies themselves are subject to exploitation.
Exploitation is a central component of sports and competitive games.
This is also applicable in other real-world competitive domains, including airport and network security, financial and energy trading, traffic control, routing, etc.
Therefore, exploring game-playing strategies that intentionally avoid the equilibrium solution and instead "learn to exploit" is a promising research direction toward more capable, adaptable, and ultimately more human-like artificial agents.
Hence, we develop a new algorithm Hierarchical Agent with Self-Play that learns to exploit the suboptimality of opponents in order to learn a wider variety of behaviors more in line with what humans might choose to display.In this work, we focus on two-player, symmetric, extensive form games of imperfect information, though generalization to more players and asymmetric games is feasible and relatively straightforward.
First, we adopt recent Proximal Policy Gradient (PPO)(?) methods in deep reinforcement learning (RL), which has been successful at handling complex games BID16 BID24 BID17 and many other fields BID20 ) Second, we aim to automatically acquire a strong strategy that generalizes against opponents that we have not seen in training.
Here we use self-play to gradually acquire more and more complex behaviors.
This technique has proven successful at solving backgammon BID27 , the game of Go , imperfect information games such as Poker BID8 BID9 , continuous control BID0 , and modern video games BID20 .In
this paper, we investigate a new method for learning strong policies on multi-player games. We
introduce Hierarchical Agent with Self-Play , our hierarchical learning algorithm that automatically learns several diverse, exploitable polices and combines them into an ensemble model that draws on the experience of the sub-policies to respond appropriately to different opponents. Then
, we show the results of some experiments on two multiplayer games: iterated Rock-Paper-Scissors and a partially observable real-time strategy game based on a popular online game generals.io (http://generals.io/). We
show that compared to conventional self-play, our algorithm learns a more diverse set of strategies and obtains higher rewards against test opponents of different skill levels. Remarkably
, it can achieve 77% win rate against the FloBot, the strongest open-sourced scripted bot on the generals.io online leaderboard.
In this paper, we investigate a novel learning approach Hierarchical Agent with Self-Play to learning strategies in competitive games and real-time strategic games by learning several opponent-dependent sub-policies.
We evaluate its performance on a popular online game, where we show that our approach generalizes better than conventional self-play approaches to unseen opponents.
We also show that our algorithm vastly outperforms conventional self-play when it comes to learning optimal mixed strategies in simpler matrix games.
Though our method has achieved good results, there are some areas which could be improved in future research.
In the future, we hope to also achieve good performance on larger versions of Generals, where games last longer and therefore learning is harder.
We would also like to investigate further the effects that our algorithm has on exploration with sparse reward.

assistant: text: We develop Hierarchical Agent with Self-play (HASP), a learning approach for obtaining hierarchically structured policies that can achieve high performance than conventional self-play on competitive real-time strategic games.