Reinforcement learning (RL) refers to a sub-field of machine learning that enables AI-based systems to take actions in a dynamic environment through trial and error to maximize the collective rewards based on the feedback generated for individual activities.

Q-learning is a model-free reinforcement learning algorithm to learn the value of an action in a particular state. It does not require a model of the environment (hence "model-free"), and it can handle problems with stochastic transitions and rewards without requiring adaptations.

There are some inherent difficulties about multi-agent deep reinforcement learning. The environment is not stationary anymore, thus the Markov property is violated: transitions and rewards do not only depend on the current state of an agent.

The purpose of reinforcement learning is for the agent to learn an optimal, or nearly-optimal, policy that maximizes the "reward function" or other user-provided reinforcement signal that accumulates from the immediate rewards. This is similar to processes that appear to occur in animal psychology.