Reinforcement Learning
Explore the fundamentals of reinforcement learning, including how agents learn from interactions with an environment to maximize rewards. Understand key concepts like states, actions, and rewards, and learn about the challenges of implementing reinforcement learning in ML.NET through custom deep learning models.
We'll cover the following...
Reinforcement learning is a type of ML that involves an agent interacting with an environment and learning to take actions that maximize a reward signal. The process involves the agent taking actions in the environment, receiving feedback in the form of rewards, and learning from the feedback to adjust its policy or strategy. The agent aims to learn the optimal policy that maximizes the cumulative reward over time.
Reinforcement learning algorithms typically use a trial-and-error approach to learn the optimal policy. The agent starts with a random or default policy and interacts with the environment, receiving feedback ...