RL Math: MDP and other algorithms

4 minute read

Notes on the mathematics of RL and stuffs.

Markov Decision Process

At the heart of every reinforcement learning (RL) problem is a Markov Decision Process. This section reviews the notations associated with this concept.


Symbol(s) Meaning(s)
t = 0, 1, … T Discrete time steps
\(S_i \in \mathcal{S}\) State in time step i
\(A_i \in \mathcal{A}\) Action taken in time step i
\(R_{i+1}\) Reward received at the begining of time step i+1
\(G_{i} =\sum_{k=0}^{\infty} \gamma^k R_{k+i+1}\) Reward-to-go from time step i
\(\pi_{i}(A_{i}\mid S_{i})\) Policy at time step i

Markov Property

A process is said to satisfy Markov Property if the state of the current time-step and the action taken in this time step determines completely the probability distribution of the state of next step. That is, \[\mathbf{Pr}(S_{t+1} = s’, R_{t+1} = r \mid S_0, A_0, R_1, S_1, …, S_t, A_t) = \mathbf{Pr}(S_{t+1} = s’, R_{t+1} = r \mid S_t, A_t)\]


Directly from the Markov property, a Markov Decision Process can be specified by two mappings.

  1. Transition probability \(\rho: \mathcal{S}\times\mathcal{A}\times\mathcal{S}\rightarrow \mathbb{R}\) which is defined as \[\rho(s’, a, s):=\mathbf{Pr}(S_{t+1} = s’\mid S_t=s, A_t=a)\]
  2. Expected reward \(r: \mathcal{S}\times\mathcal{A}\times\mathcal{S}\rightarrow \mathbb{R}\) which is defined as \[r(s’, a, s):=\mathbf{E}(R_{t+1}\mid S_{t+1} = s’ S_t=s, A_t=a)\]

In Sutton and Barto (1998), an alternative formulation is that a single conditional distribution function is used:

\begin{equation} \rho(s’, r \mid s, a) := \mathbf{Pr}(S_{i+1}=s’, R_{i+1}=r \mid S_i=s, A_i=a) \end{equation}

Additionally, a MDP can have finite horizon, that is a limited number of time step or an infinite horizon and a discount factor.

It is often useful to indicate terminal states. These are states from which only self-looping action is available.

Finally, note that we implicitly assume transition function \(\rho\) and reward function \(r\) do not depend on time.

Finite MDP and Exact solution

A finite MDP can be solved relatively easily using Dynamic Programming (Bellman (2013)). In fact, using DP to solve MDP is fairly common strategy to solve optimal control problems (Bertsekas (2007)).

Here I briefly explain Value Iteration, a classical way to solve MDP. Though, be warned that nobody does it nowaday because it doesn’t scale well. Define value function and action-value function at time step \(i\)

\begin{equation} \begin{aligned} v_i(s) &:= \mathbf{E}_{\pi^*_{i}} \left[G_i\mid S_i=s\right] \\
q_i(s, a) &:= \mathbf{E}_{\pi^*_{i}} \left[G_i\mid S_i=s, A_i=a\right] \\
\end{aligned} \end{equation}

where \(\pi^{*}_{i}\) is the optimal policy at time step \(i\).

The famous principle of optimality states the following exact relation

\begin{equation} v_i(s)=\max_{a\in \mathcal{A}}q_i(s, a) \end{equation}

Using this principle, the finite horizon MDP can be solved via the following recursive relations

\begin{equation} \begin{aligned} v_{T}(s) &= 0\\
v_{i-1}(s) &= \max_{a\in \mathcal{A}} \left[ \sum_{s’\in \mathcal{S}}\rho(s, a, s’) \left< r(s,a, s’) + \gamma v_i(s’) \right>\right] \end{aligned} \end{equation}

Infinite MDP

Good news. The subscript indicating time step disappear. Both value and action-value functions are time independent now. This leads to an unique set of optimal functions.

Bad new. Theoretically, these functions might not exist. In fact, the commonly used discount factor is include precisely to forms upper bounds and ensures convergence and existence.

Comments on MDP

Policty \(\pi\) is a mapping from the current state to action. In another word, it is the controller. In practice, a robot controller does not have access to the state of the problem; instead, it can only use the measure outputs \(\mathbf{y}\) as shown in the below figure.

The question is then, how applicable is MDP to an actual control problem? It seems to me the answer is somewhat murky. Basically most control algorithms learn controllers that do not require states as inputs but observation/measure outputs instead. The issue with that is that the MDP theory no longer applied and that there is no more theoretical guarantee on the final policy or the algorithm that computes that policy.

An extension of MDP that tries to remain theoretically inclined is Partially-Observable MDP. Most notably in mobile robots.

A “practical” Markov Decision Process

At the momement, most RL framework and algorithms assume a similar structure of the environment. Instead of defining a MDP or POMDP explicity, say with transitional and reward probabilities, a black-box environment is given. This environment can be stepped forward using action and produces reward and observation. To be mathematically precise, it’s a POMDP, not MDP as commonly described in the literature.

The most popular framework is OpenAI gym. This library provides a common interface to defining RL environment, which is very convenient. A basic environment look like this:

# example from https://gym.openai.com/docs/#environments
import gym
env = gym.make('CartPole-v0')
for i_episode in range(20):
    observation = env.reset()
    for t in range(100):
    action = env.action_space.sample()
    observation, reward, done, info = env.step(action)
    if done:
        print("Episode finished after {} timesteps".format(t+1))

To define a new environment, I find this is a good instruction to follow. Basically it iss sufficient to follow the interface defined by the abstract class gym.core.Env. Almost all RL algorithms expect this interface, thus it is very easy to test out different algorithms on the problem.

Also noted from my own experiment RlTrialMovingFastToContact, the environment’s definition has a major effect on whether RL algorithms can effectively solve it. There are a few considrations:

  • Actions and observation should be normalized to around 1.
  • For discrete-time problem, a shorter horizon (~100 steps) is easier to learn comparing to a much longer horizon of 1000 steps.
  • Reward shaping is very important to achieving good performance.

RL Algorithms

Here let’s review the juicy part: the math of RL algorithms. Now I must note that understanding the math is probably 30% of the task. To implement a RL algorithm, the implementation and the tricks constitute the rest of the work.




Bellman, Richard. 2013. *Dynamic programming*. Courier Corporation.
Bertsekas, Dimitri P. 2007. *Dynamic Programming and Optimal Control*. Athena Scientific Belmont, MA.
Sutton, R S, and A G Barto. 1998. "Reinforcement learning: an introduction." MIT press. <https://doi.org/10.1109/TNN.1998.712192>.

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