SimplifAIng

In previous blogposts, we saw how supervised and unsupervised learnings have their own types and how they are different from one another. To understand the difference, we had taken a small and simple example and also identified if and how certain model types could be used interchangeably in specific scenarios.

In this blogpost, we will see the different types of reinforcement learning and use the same strategy as before, to understand the different types of reinforcement learning and their alternate use in particular cases.

Reinforcement Learning: A Brief Overview

Reinforcement Learning (RL) is a subfield of machine learning and artificial intelligence that focuses on training agents to make decisions by interacting with an environment. In RL, an agent learns to take actions in order to maximize the cumulative reward over time.

The learning process involves trial and error, where the agent takes actions in different situations (or states) and receives feedback in the form of rewards or penalties. The agent’s goal is to learn an optimal policy, which is a mapping from states to actions that leads to the highest expected cumulative reward.

Reinforcement learning differs from supervised and unsupervised learning as it does not rely on labeled data or predefined patterns. Instead, the agent learns by interacting with the environment and adjusting its behavior based on the feedback received.

Key components of a reinforcement learning setup include:

• Agent: The decision-making entity that interacts with the environment.
• Environment: The context or world in which the agent operates.
• State: A representation of the current situation or context of the agent within the environment.
• Action: The decisions or moves made by the agent in response to the current state.
• Reward: Immediate feedback received by the agent after taking an action, which can be positive, negative, or zero.
• Policy: The strategy or set of rules that the agent follows to decide which actions to take in each state.

There are various reinforcement learning algorithms, such as Q-learning, SARSA, and Policy Gradient, which help agents learn the optimal policy for different types of problems. These algorithms can be broadly categorized into model-free and model-based methods, and their subtypes.

Model-Based and Model-Free Reinforcement Learning Reinforcement Learning (RL) algorithms

Model-Based Reinforcement Learning

Model-based RL methods learn an approximation of the environment’s dynamics and reward function, using this knowledge to plan actions. They often require fewer samples and are more data-efficient compared to model-free methods. However, they may struggle with complex environments, where learning an accurate model becomes challenging. In the automotive context, model-based methods can be used for trajectory planning, route optimization, or fuel consumption minimization.

Model-Based Reinforcement Learning Subtypes

Model-based RL algorithms can be classified into two main categories: planning algorithms and learning algorithms.

• Planning algorithms: These algorithms use an existing model of the environment to plan the optimal actions. Examples of planning algorithms include:
  • Value Iteration (VI): An iterative algorithm that computes the optimal value function and subsequently derives the optimal policy.
  • Policy Iteration (PI): An algorithm that alternates between policy evaluation (computing the value function for a given policy) and policy improvement (updating the policy based on the current value function) until convergence.
  • Monte Carlo Tree Search (MCTS): A search algorithm that uses Monte Carlo simulations to build a search tree and estimate the value of different actions.
• Learning algorithms: These algorithms learn the model of the environment from the agent’s interactions. Some examples include:
  • Model Learning with Q-Learning: This approach involves learning a model of the environment’s dynamics alongside Q-values, which estimate the expected cumulative reward of state-action pairs.
  • Dyna-Q: An integrated approach that combines model learning, planning, and direct reinforcement learning. Dyna-Q learns the model of the environment and uses it for planning and updating the Q-values.

In the context of reinforcement learning, “Q” refers to the action-value function or Q-function. The Q-function, denoted as Q(s, a), estimates the expected cumulative reward an agent can obtain by taking a specific action “a” in a given state “s” and following an optimal policy thereafter. Essentially, it measures the goodness or value of a particular action in a specific state.

Model-Free Reinforcement Learning

Model-free methods, including value-based and policy-based approaches, learn directly from the agent’s interaction with the environment without explicitly modeling the environment. They can be more robust to inaccuracies in the model, but they typically require more samples and are less data-efficient compared to model-based methods. In automotive applications, model-free methods can be used for tasks like lane keeping, adaptive cruise control, or collision avoidance.

Model-Free Reinforcement Learning Subtypes

Model-free RL algorithms can be further categorized into two main subtypes: value-based methods and policy-based methods.

• Value-based methods: These methods learn the value function, which estimates the expected cumulative reward from each state or state-action pair. Popular value-based algorithms include:
  • Q-Learning: An off-policy algorithm that learns the optimal Q-values for state-action pairs, which are used to derive the optimal policy.
  • SARSA (State-Action-Reward-State-Action): An on-policy algorithm that learns Q-values by updating estimates based on the current state, action, reward, next state, and next action.
• Policy-based methods: These methods learn the policy directly, which is a mapping from states to actions. Some examples of policy-based algorithms are:
  • REINFORCE (Monte Carlo Policy Gradient): An algorithm that learns the policy by directly estimating the gradient of the expected cumulative reward with respect to the policy parameters.
  • Proximal Policy Optimization (PPO): A policy-based method that constrains the policy updates to avoid overly large updates, making the learning process more stable and robust.

In addition to these subtypes, there are hybrid methods that combine value-based and policy-based approaches, known as actor-critic methods. These algorithms use a policy-based component (the actor) to decide which action to take and a value-based component (the critic) to evaluate the chosen actions. Examples of actor-critic methods include:

• Advantage Actor-Critic (A2C): An algorithm that uses the advantage function (the difference between the value function and the state-action value function) to update both the policy and the value function.
• Deep Deterministic Policy Gradient (DDPG): An off-policy actor-critic method that can handle continuous action spaces by using a deterministic policy and a state-action value function.

A Simple Example to Differentiate the Models

Let’s consider a simple automotive example of an autonomous vehicle (AV) learning to drive safely and efficiently in a one-lane road environment. The goal of the AV is to maintain a safe distance from the vehicle in front while minimizing fuel consumption.

• Model-Based Reinforcement Learning Subtypes
  • Planning algorithms
    • Value Iteration: The AV uses a given model of the environment to compute the value of each possible state (distance to the vehicle in front, speed, etc.). It iteratively updates these values until convergence and then derives the optimal policy (accelerate, maintain speed, or brake) for each state.
    • Policy Iteration: The AV starts with an initial policy and iteratively evaluates and improves it using the given environment model. It computes the value function for the current policy and updates the policy based on the computed values.
  • Learning algorithms
    • Model Learning with Q-Learning: The AV learns the environment model (how actions affect the distance to the vehicle in front) and Q-values simultaneously. It updates the Q-values based on the learned model and uses them to decide which actions to take.
    • Dyna-Q: The AV learns the environment model while also updating the Q-values. It uses the learned model for planning and updates the Q-values based on the simulated experience.
• Model-Free Reinforcement Learning Subtypes
  • Value-based methods
    • Q-Learning: The AV learns the Q-values of state-action pairs (e.g., accelerating when the distance to the vehicle in front is X meters) by interacting with the environment. It chooses the action with the highest Q-value in each state.
    • SARSA: Similar to Q-Learning, the AV learns the Q-values of state-action pairs. However, it updates the Q-values based on the actual actions it takes, making it an on-policy algorithm.
  • Policy-based methods
    • REINFORCE: The AV learns the policy directly by adjusting the policy parameters in the direction that maximizes the expected cumulative reward. For example, it might increase the probability of accelerating when the distance to the vehicle in front is large and decrease the probability of accelerating when the distance is small.
    • Proximal Policy Optimization (PPO): The AV learns the policy directly while constraining the updates to avoid large policy changes. This makes the learning process more stable, allowing the AV to find a good balance between exploration and exploitation.
• Hybrid Methods
  • Advantage Actor-Critic (A2C): The AV uses a policy-based component (the actor) to decide which actions to take and a value-based component (the critic) to evaluate the chosen actions. It updates both the policy and the value function to optimize the driving behavior.
  • Deep Deterministic Policy Gradient (DDPG): The AV employs a deterministic policy (the actor) and a state-action value function (the critic) to handle continuous action spaces, such as precise acceleration or braking levels. The actor decides the actions, and the critic evaluates and helps update the actor’s decisions.

Interchangeability and Choosing the Right Method

In some cases, model-based and model-free methods can be used interchangeably, depending on the problem’s complexity, available resources, and required performance. Model-based methods may be preferred when a reasonably accurate model of the environment is available or can be learned efficiently. Model-free methods might be more appropriate for complex or rapidly changing environments where modeling is challenging.

In autonomous driving, both methods can be employed for different aspects of the system, or they can be combined using an actor-critic architecture, where the actor represents the policy and the critic represents the value function. This hybrid approach can leverage the strengths of both model-based and model-free methods.

To summarize, in this blogpost we were able to identify the differences and purposes of model-based and model-free reinforcement learning methods and we can choose the most suitable approach for a specific automotive application. Depending on the task and the environment, each type of method may offer unique advantages, and in some cases, they can be used interchangeably or combined to achieve the best performance.