Go can be used for developing reinforcement learning models. Reinforcement learning is a subfield of machine learning that involves training agents to make decisions based on rewards and penalties. This involves interactions with an environment in which the agent learns to maximize a cumulative reward signal over time.
Go has several libraries and frameworks that can be used for reinforcement learning, including Gorgonia, a library for building and training neural networks, and RLGo, a reinforcement learning library that provides implementations of popular algorithms such as Q-Learning and Deep Q-Learning.
Additionally, Go's concurrency features make it well-suited for reinforcement learning, as it allows for efficient parallelization of computations.
Some popular applications of reinforcement learning in Go include game playing, robotics, and autonomous vehicles.