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Reinforcement Learning
Let's say we have an agent in an unknown environment and this agent can obtain some rewards by interacting with the environment.
The agent is tasked to take actions so as to maximize cumulative rewards. In reality, the scenario could be a bot playing a game to achieve high scores, or a robot trying to complete physical tasks with physical items; and not just limited to these.
Like humans, RL agents learn for themselves to achieve successful strategies that lead to the greatest long-term rewards.
This kind of learning by trial-and-error, based on rewards or punishments, is known as reinforcement learning (RL).
TensorTrade is an open-source Python framework for building, training, evaluating, and deploying robust trading algorithms using reinforcement learning.
https://github.com/tensortrade-org/tensortrade
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