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.
