Reinforcement learning is more suitable for simulated environments, and environments in which making mistakes has very low to zero cost. For the applications listed below, list whether reinforcement learning is suitable or not, explaining why. Feel free to include details that better define the environment or the conditions under which reinforcement learning would or would not be suitable. Medical diagnosis of cancer patients Recommending the next action to an auto-pilot Exploring an area with limited space to explore Identifying plants based on their physical features Recommending buying/selling decisions for stocks Controlling a robot arm that assembles toys Training a self-driving car in a simulated environment Training a self-driving car in a real environment (driving in real streets)

Reinforcement learning is more suitable for simulated environments, and environments in which making mistakes has very low to zero cost. For the applications listed below, list whether reinforcement learning is suitable or not, explaining why. Feel free to include details that better define the environment or the conditions under which reinforcement learning would or would not be suitable. Medical diagnosis of cancer patients Recommending the next action to an auto-pilot Exploring an area with limited space to explore Identifying plants based on their physical features Recommending buying/selling decisions for stocks Controlling a robot arm that assembles toys Training a self-driving car in a simulated environment Training a self-driving car in a real environment (driving in real streets)