# Quick Notes on Reinforcement Learning

## Introduction

In this series of blog posts, I intend to write my notes as I go through Richard S. Suttonâ€™s excellent *Reinforcement Learning: An Introduction* (1).

I will try to formalise the maths behind it a little bit, mainly because I would like to use it as a useful personal reference to the main concepts in RL. I will probably add a few remarks about a possible implementation as I go on.

## Relationship between agent and environment

### Context and assumptions

The goal of reinforcement learning is to select the best actions availables to an agent as it goes through a series of states in an environment. In this post, we will only consider *discrete* time steps.

The most important hypothesis we make is the *Markov property:*

At each time step, the next state of the agent depends only on the current state and the current action taken. It cannot depend on the history of the states visited by the agent.

This property is essential to make our problems tractable, and often holds true in practice (to a reasonable approximation).

With this assumption, we can define the relationship between agent and environment as a *Markov Decision Process* (MDP).

A *Markov Decision Process* is a tuple \((\mathcal{S}, \mathcal{A},
\mathcal{R}, p)\) where:

\(\mathcal{S}\) is a set of

*states*,\(\mathcal{A}\) is an application mapping each state \(s \in \mathcal{S}\) to a set \(\mathcal{A}(s)\) of possible

*actions*for this state. In this post, we will often simplify by using \(\mathcal{A}\) as a set, assuming that all actions are possible for each state,\(\mathcal{R} \subset \mathbb{R}\) is a set of

*rewards*,and \(p\) is a function representing the

\[\begin{align} p &: \mathcal{S} \times \mathcal{R} \times \mathcal{S} \times \mathcal{A} \mapsto [0,1] \\ p(s', r \;|\; s, a) &:= \mathbb{P}(S_t=s', R_t=r \;|\; S_{t-1}=s, A_{t-1}=a), \end{align} \]*dynamics*of the MDP:such that \[ \forall s \in \mathcal{S}, \forall a \in \mathcal{A},\quad \sum_{s', r} p(s', r \;|\; s, a) = 1. \]

The function \(p\) represents the probability of transitioning to the state \(s'\) and getting a reward \(r\) when the agent is at state \(s\) and chooses action \(a\).

We will also use occasionally the *state-transition probabilities*:

### Rewarding the agent

The *expected reward* of a state-action pair is the function

The *discounted return* is the sum of all future rewards, with a multiplicative factor to give more weights to more immediate rewards: \[ G_t := \sum_{k=t+1}^T \gamma^{k-t-1} R_k, \] where \(T\) can be infinite or \(\gamma\) can be 1, but not both.

## Deciding what to do: policies

### Defining our policy and its value

A *policy* is a way for the agent to choose the next action to perform.

A *policy* is a function \(\pi\) defined as

In order to compare policies, we need to associate values to them.

The *state-value function* of a policy \(\pi\) is

We can also compute the value starting from a state \(s\) by also taking into account the action taken \(a\).

The *action-value function* of a policy \(\pi\) is

### The quest for the optimal policy

## References

- R. S. Sutton and A. G. Barto, Reinforcement learning: an introduction, Second edition. Cambridge, MA: The MIT Press, 2018.