Understanding Reinforcement Learning: A Comprehensive Guide

4 min readNov 25, 2023

Understanding Reinforcement Learning: A Comprehensive Guide

Intro to Reinforcement Learning

Reinforcement learning (RL) represents a unique branch of machine learning where agents learn by interacting with their environment. Unlike supervised learning, which depends on labeled data and explicit guidance, or unsupervised learning, which lacks direct feedback mechanisms, RL is akin to how animals and humans learn — through trial and error and understanding the outcomes of their actions.

Imagine teaching your dog, Buster, to roll over in a fun-filled training session. Each wiggle and attempt is met with cheers and treats, sparking Buster’s excitement. He quickly catches on: rolling over equals yummy treats and praise! No roll, no treat, but no scolding either. Before you know it, Buster’s a rolling pro, all tail wags and eagerness. This isn’t just training; it’s a joyous game of learning and treats — reinforcement learning at its most delightful and dog-friendly!

Key Components of Reinforcement Learning

Agent

In RL, the agent is the entity that learns and makes decisions. It’s akin to a player in a game, learning to make moves to achieve its goals.

The agent observes the state of the environment, decides on an action to take, and then receives feedback in the form of rewards. Through this process, it learns a policy, which is a strategy for selecting actions based on the current state.

An agent’s characteristics can vary. It might be simple, with limited memory and decision-making capabilities, or complex, with the ability to process and remember extensive information about its environment.

Environment

The environment is everything in the RL setting that the agent interacts with. It’s the ‘game board’ on which the agent operates.

Environments can be deterministic (the same action always leads to the same outcome) or stochastic (actions have probabilistic outcomes). They can also be discrete (with a finite number of states and actions) or continuous (with an infinite number of possible states and actions).

The environment provides the states and rewards to the agent and changes in response to the agent’s actions.

State

A state represents a snapshot of the environment at a given time. It’s the context within which the agent makes a decision.

States can range from simple (few variables) to complex (many variables). The completeness of state information can vary — in some cases, the agent sees the entire environment (fully observable), and in others, it sees only a part (partially observable).

The state reflects the agent’s perspective of the environment, which influences how it makes decisions.

Action

Actions are the set of possible moves or decisions the agent can make in each state.

Actions can be discrete (like moving left or right) or continuous (like adjusting a thermostat). They can also be deterministic (predictable outcomes) or stochastic (uncertain outcomes).

The choice of action directly affects the agent’s future state and the reward it receives.

Reward

A reward is feedback from the environment in response to the agent’s action. It’s a measure of success or failure in achieving the goal.

Rewards can be positive (reinforcing a good action) or negative (penalizing a bad action). They can be immediate (short-term consequences) or delayed (long-term consequences).

Rewards guide the learning process of the agent. The agent aims to maximize the cumulative reward over time, which shapes its policy.

Goals and Methods in Reinforcement Learning

The primary aim in RL is to establish a policy that maximizes the total accumulated reward over time. This involves two principal methods:

Value-Based Methods

These methods estimate the expected reward for each state or state-action pair, with the agent choosing the action of highest predicted value. Notable methods include Q-learning, SARSA, and Deep Q-Networks (DQN).

Policy-Based Methods

These approaches optimize the policy directly without relying on value estimation. The agent learns a policy that might be stochastic or deterministic, as seen in methods like REINFORCE, Actor-Critic, and Proximal Policy Optimization (PPO).

Applications and Examples of Reinforcement Learning

RL finds application across various sectors like robotics, gaming, finance, healthcare, and education. Some landmark examples are:

Wayve

Historically, self-driving car development relied on predefined logic rules, but this approach struggles with the myriad of unpredictable situations encountered on public roads. Deep reinforcement learning offers a more scalable solution. Wayve, a UK company, has explored this since 2018. In their paper “Learning to Drive in a Day” they detail using deep reinforcement learning with a single camera image as input. The model’s reward was based on the distance driven without human intervention. Initially trained in a simulation, the model was then tested in the real world on a 250-meter road stretch. As Wayve’s technology evolves, reinforcement learning remains a key component in their motion planning process, aiding in creating viable paths for autonomous vehicles.

AlphaGo

A deep learning program that mastered Go through neural networks and Monte Carlo tree search, defeating world champion Lee Sedol.

OpenAI Five

A team of neural networks trained to play Dota 2, using techniques like self-play and PPO, and succeeding against top players.

DeepMind Lab

One of my personal favorites! A 3D platform aiding in agent learning across complex environments, suitable for research in areas such as navigation and memory.