Reinforcement learning (RL) is a type of machine learning that involves training an agent to make decisions in an environment in order to maximize a reward signal. To develop a model that can learn to play a game or control a robotic arm using RL, you can follow these general steps:

Define the problem: Clearly define the task you want the agent to perform, such as playing a specific game or controlling a robotic arm.

Define the environment: Create a simulation of the environment in which the agent will operate, such as a game engine or a physics simulator for a robotic arm.

Define the reward function: Specify the reward signal that the agent will try to maximize. This could be a score in a game or the success of a task performed by a robotic arm.

Choose an RL algorithm: There are many different RL algorithms to choose from, such as Q-learning, SARSA, and PPO. Each algorithm has its own strengths and weaknesses, so choose one that is well-suited to your problem.

Train the agent: Use the chosen RL algorithm to train the agent in the environment, using the defined reward function.

Evaluate the agent: Test the agent to see how well it performs in the task.

Refine the model: Based on the results of the evaluation, you may need to adjust the reward function, the environment, or the RL algorithm in order to improve the agent's performance.

It's also important to note that Reinforcement Learning is a complex area of machine learning that requires a good understanding of the concepts and mathematical background. It may be helpful to consult with experts in the field and read up on the literature before attempting to develop a model on your own.