Markov Decision Process Python Library

## Project description

![MDP Image](https://cdn-images-1.medium.com/max/1200/1*QuBOz2yQ5Fy6YnZyvSPXzw.png)

## Markov: Simple Python Library for Markov Decision Processes
#### Author: Stephen Offer

Markov is an easy to use collection of functions and objects to create MDP
functions.

Markov allows for synchronous and asynchronous execution to experiment with
the performance advantages of distributed systems.

#### States:

- Reward, Terminal State, Actions, Value, Previous States, Next States, State
Policy Probabilities.

#### Policies:

- Greedy Policy
- e-Greedy Policy
- More to come...

#### Algorithms:

- Dynamic Programming
- Linear coming soon

#### Optimizers:

- Value/Policy Iteration
- More to come...

#### Environments:

- Gridworld (ASCII, PyGame coming soon)
- Gym coming soon
- More to come...

### Example:
```python
import numpy as np
import argparse

from markov import GreedyPolicy
from markov.envs.gridworld import GridWorld

def value_iteration(K=1,discount_factor=1.):

env = GridWorld()

P = GreedyPolicy(env)

values = np.zeros(env.n_states)

for k in range(K):
for state in env.states:
v = 0
for i, action in enumerate(state.actions):
policy = state.policy[i]
next_state = action(env, state.action_args)
r = next_state.reward
v += policy * (r + discount_factor * next_state.value)

values[state.index] = v

for state in env.states:
state.value = values[state.index]

env.print()

def main():
parser = argparse.ArgumentParser()
type=int,default=1)
args = parser.parse_args()
k = args.k

value_iteration(k)

if __name__ == "__main__":
main()

```

#### Contributors Welcome