Reinforcement Learning for Execution and Trading

Reinforcement Learning

• RL is a mathematical framework for experience-driven autonomous learning.
• The essence of RL is learning through interaction.

Reinforcement Learning

Reinforcement Learning

• An autonomous agent, controlled by a machine learning algorithm, observes a state s(t) from its environment at timestamp t.
• The agent interacts with the environment by taking an action a(t) in state s(t).
• When the agent takes an action, the environment and the agent transition to a new state s(t+1) based on the current state and the chosen action.

Reinforcement Learning

• The goal of the agent is to learn a policy π that maximizes the expected return.

Execution in RL Context