What is policy in reinforcement learning?

Reinforcement learning is a type of machine learning where a agent like a robot or a program learns to make decisions by interacting with an environment. The agent takes actions, observe the results and receives rewards or penalties based on that. Its goal is to figure out the best way to act in order to maximize its total rewards over time. A policy is simply the strategy or rulebook that the agent follows to decide what action to take in any given situation.

Based on current environment or observations this policy helps the agent to choose the next move. For example if a robot is learning to walk then policy will guide it on how to move its legs depending on their current position. This process of learning makes reinforcement learning useful in many real-world situations like:

• Teaching self-driving cars to navigate
• Training chatbots to respond helpfully
• Help robots to learn how to walk or pick things

How Agent Learn a Policy?

In reinforcement learning, policies are learned through trial and error where the agent try different actions, sees what works (reward) and what doesn’t (penalty) and slowly gets better at making decisions. Over time it learns which actions are more helpful in different situations. There are two common ways an agent learns a policy:

Value-Based Methods

In value-based learning the agent tries to figure out how good each situation is. It does this by learning something called a value function which tells the agent:

"If I’m in this situation (or state), how much reward can I expect in the future?”

Once it knows that it can choose actions that take it to higher value states. One widely used example of this is Q-learning. In Q-learning the agent builds a table of Q-values which show how good a certain action is in a certain state. Over time the agent updates this table based on experience.

Policy-Based Learning

In policy-based learning the agent skips the step of figuring out the value of a state. Instead it directly learns a policy. This method is used when the best decision can’t be easily found just by checking values. REINFORCE is a basic policy gradient method where an agent updates its policy to favor actions that lead to better rewards.

Types of Policies

Policies in reinforcement learning can be either deterministic or stochastic :

Deterministic Policy

• A deterministic policy always selects the same action when the agent is in a particular state. This means that for every situation the agent has a fixed rule to follow, with no randomness involved.
• For example if an agent is programmed to always move forward when it sees an obstacle it will do so every time that situation occurs.

Stochastic Policy

• Stochastic policy allows the agent to choose actions based on probabilities. This means that for the same state the agent can select different actions at different times. These are used when exploration or uncertainty plays an important role in learning the best behavior over time.
• For example it can decide to move left 70% of the time and right 30% of the time when faced with a certain scenario.

By using value-based methods, policy-based methods or a combination of both, agents can develop strategies that help them achieve their goals efficiently.