# Markov Decision Process

A sequential decision problem for a fully observable, stochastic environment with a Markovian transition model and additive rewards is called a Markov decision process. It consists of a set of states, with initial state $$s_0$$, a set $$ACTIONS(s)$$ of actions in each state, a transition model $$P(s’|s, a)$$, and a reward function $$R(s)$$.

A policy, denoted $$\pi$$, specifies what the agent should do in any state $$s$$. This action is denoted by $$\pi(s)$$. The optimal policy $$\pi^*$$ yields the highest expected utility.

The careful balancing of risk and reward is a characteristic of MDPs that does not arise in deterministic search problems.

## Optimality in Markov Decision Processes

### Finite Horizon

\begin{equation} E\left( \sum_{t=0}^{h} r_t \right) \end{equation}

### Infinite Horizon

\begin{equation} E\left( \sum_{t=0}^{\infty} \gamma^t r_t \right) \end{equation}

### Average-reward

\begin{equation} \lim_{h \rightarrow \infty} E\left( \sum_{t=0}^{h} \frac{1}{h} r_t \right) \end{equation}

## Learning Performance (Kaelbling {\it et al.}, 1996)

1. Asymptotic convergence:

\begin{equation} \pi_n \rightarrow \pi^* \text { as } n \rightarrow \infty \end{equation}

1. PAC:

\begin{equation} P(N_{errors} > F(\cdot, \epsilon, \delta)) \le \delta \end{equation}

Does not give any guarantee about the policy while it is learning

1. Regret (e.g. bound $$B$$ on total regret):

\begin{equation} \mathrm{max} \sum_{t=0}^{T} r_{tj} - r_t < B \end{equation}

No notion of “many small mistakes”, or “few major mistakes”.

1. Uniform-PAC

unifies notion of PAC and regret (Dann {\it et al.}, 2017)

# Bibliography

Kaelbling, L. P., Littman, M. L., & Moore, A. W., Reinforcement learning: a survey, Journal of artificial intelligence research, 4(), 237–285 (1996).

Dann, C., Lattimore, T., & Brunskill, E., Unifying pac and regret: uniform pac bounds for episodic reinforcement learning, In , Advances in Neural Information Processing Systems (pp. 5713–5723) (2017). : .

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