Frontiers of RL

You have now seen the theory behind and application of the most useful RL techniques. Yet, RL is a moving field. This book cannot cover all of the current trends that might be interesting to practitioners, but it can highlight some that are particularly useful for practitioners in the financial industry.

Multi-agent RL

Markets, by definition, include many agents. Lowe and others, 2017, Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments (see https://arxiv.org/abs/1706.02275), shows that reinforcement learning can be used to train agents that cooperate, compete, and communicate depending on the situation.

Multiple agents (in red) working together to chase the green dots. From the OpenAI blog.

In an experiment, Lowe and others let agents communicate by including a communication vector into the action space. The communication vector that one agent outputted was then made available to other agents. They showed that the agents learned to communicate to solve a...