Seth Barrett

Daily Blog Post: August 12th, 2023


August 12th, 2023

Reinforcement Learning: Navigating the World through Trial and Error

Welcome back to our Advanced Machine Learning series! In this blog post, we'll explore the fascinating world of Reinforcement Learning (RL), where AI agents learn to interact with their environment through trial and error, guided by the pursuit of rewards.

What is Reinforcement Learning?

Reinforcement Learning is a type of machine learning that deals with sequential decision-making problems. In RL, an agent interacts with an environment, taking actions to maximize cumulative rewards. The agent learns from the feedback provided by the environment, adjusting its actions to achieve its objectives.

Core Components of Reinforcement Learning

  1. Markov Decision Process (MDP): MDP is a formal framework used to model sequential decision-making problems in RL. It consists of states, actions, a transition model, rewards, and a discount factor. The agent aims to find a policy that maps states to actions, maximizing the expected cumulative reward.
  2. Policy Learning: A policy in RL is a strategy that defines the agent's actions in each state. Policy learning algorithms, such as Q-learning and Deep Q Networks (DQNs), aim to find the optimal policy that maximizes the expected rewards.
  3. Value Iteration: Value iteration is an algorithm used to compute the value function of states in an MDP. The value function represents the expected cumulative reward that the agent can achieve from each state

Applications of Reinforcement Learning

Reinforcement Learning has found numerous applications, including:

  • Game Playing: RL agents have achieved superhuman performance in games like Chess, Go, and video games.
  • Robotics: RL is used to train robots to perform complex tasks, such as grasping objects and navigation.
  • Recommendation Systems: RL can optimize recommendation systems by learning from user interactions.
  • Autonomous Vehicles: RL enables autonomous vehicles to make decisions in dynamic environments.
  • Implementing Reinforcement Learning with Julia and Flux.jl

    Let's explore how to implement Q-learning, a popular RL algorithm, using Julia and Flux.jl.

    # Load required packages
    using Flux
    using Random
    # Define Q-learning function
    function q_learning(env, α, γ, ε, episodes)
        q_values = Dict()
        for episode in 1:episodes
            state = env.reset()
            done = false
            while !done
                if rand() < ε
                    action = rand(1:env.n_actions)
                    action = argmax(get_q_value(q_values, state, env.n_actions))
                new_state, reward, done = env.step(action)
                q_values[(state, action)] = get_q_value(q_values, state, action) + α * (reward + γ * maximum(get_q_value(q_values, new_state, env.n_actions)) - get_q_value(q_values, state, action))
                state = new_state
        return q_values


    Reinforcement Learning allows AI agents to navigate the world through trial and error, learning to make decisions that lead to the highest rewards. In this blog post, we've explored the core components of RL, including the Markov Decision Process, policy learning, and value iteration, and implemented Q-learning with Julia and Flux.jl.

    In the next blog post, we'll venture into the realm of Generative Models, where we'll explore techniques like Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) to create new data samples and unleash creativity in AI. Stay tuned for more exciting content on our Advanced Machine Learning journey!