Deep Reinforcement Learning

Instead of learning from a fixed, labeled dataset, the model here learns by doing things, making decisions, watching what happens, and adjusting based on whether things went well or badly. Think of it like learning a new video game through trial and error instead of reading the manual first.

Agents, Environments, and Rewards

1. Agents

The agent is the decision-maker in a reinforcement learning system. It interacts with the environment, observes the current state, and selects actions with the goal of maximizing long-term reward.

2. Environments

The environment is the system or world the agent operates in, such as a game, robot, or simulation. It responds to the agent’s actions by changing states and providing feedback.

3. Rewards

Rewards are feedback signals returned by the environment after each action. They indicate how good or bad an action was and guide the agent toward better decisions over time.

Markov Decision Processes

A Markov Decision Process, is just the formal way of writing down that whole agent and environment setup. It models sequential decision-making using states, actions, rewards, and transitions, giving reinforcement learning the structure it needs to evaluate decisions over time.

The "Markov" part means one important assumption: the next state only depends on the current state and action, not the entire history before it. States describe the current situation and hold all the info relevant for making a decision, and actions are the choices available to the agent.

Deep Q-Networks (DQN)

Q-learning, the idea that came before DQN, estimates how good it is to take a certain action in a certain state, called a Q-value. This works fine when there are only a few possible states, but it breaks down fast in environments with a huge number of situations, like video games or robotics, where you simply cannot track every state one by one. 

That is exactly the gap DQN closes. It uses deep learning to estimate those Q-values for environments way too large and complex for regular Q-learning to handle. Here is roughly how training goes: 

• The agent interacts with the environment and collects experiences 
• It samples a mini-batch of past experiences from a stored replay buffer 
• It computes target Q-values and updates the network through gradient descent 
• Every so often, the weights get copied over to a separate target network just to keep training stable

That replay buffer trick, learning from a mix of old and new experiences instead of only the latest one, is a big part of why DQN actually works well in practice.