Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment.
The agent receives feedback in the form of rewards or penalties for its actions, allowing it to improve its decision-making over time. This approach enables the agent to learn optimal strategies for achieving goals in complex, dynamic environments without explicit programming of every possible scenario.
Reinforced learning is used in algorithmic trading to predict the success of trades and train a model that can automatically trade.
A popular tool to use reinforced learning for trading strategies is FinRL.
Learn more about AI trading in Trading Strategy learning resources.
See also:
Machine learning
Trading strategy