  In this paper, reinforcement learning is applied to the problem of optimizing
market making. A multi-agent reinforcement learning framework is used to
optimally place limit orders that lead to successful trades. The framework
consists of two agents. The macro-agent optimizes on making the decision to
buy, sell, or hold an asset. The micro-agent optimizes on placing limit orders
within the limit order book. For the context of this paper, the proposed
framework is applied and studied on the Bitcoin cryptocurrency market. The goal
of this paper is to show that reinforcement learning is a viable strategy that
can be applied to complex problems (with complex environments) such as market
making.
