#reinforce
A 'plug and play' reinforcement learning library in Python.
Infers a Markov Decision Process from data and solves for the optimal policy.
Implementation based on Andrew Ng's notes.
##Motivation
scikit-learn provides excellent tools for supervised and unsupervised learning but explicitly does not deal with reinforcement learning.
##Example Usage
observations = [
{ 'state_transitions': [
{ 'state': 'low', 'action': 'climb', 'state_': 'mid' },
{ 'state': 'mid', 'action': 'climb', 'state_': 'high' },
{ 'state': 'high', 'action': 'sink', 'state_': 'mid' },
{ 'state': 'mid', 'action': 'sink', 'state_': 'low' },
{ 'state': 'low', 'action': 'sink', 'state_': 'bottom' }
],
'reward': 0
},
{ 'state_transitions': [
{ 'state': 'low', 'action': 'climb', 'state_': 'mid' },
{ 'state': 'mid', 'action': 'climb', 'state_': 'high' },
{ 'state': 'high', 'action': 'climb', 'state_': 'top' },
],
'reward': 0
}
]
trap_states = [
{
'state_transitions': [
{ 'state': 'bottom', 'action': 'sink', 'state_': 'bottom' },
{ 'state': 'bottom', 'action': 'climb', 'state_': 'bottom' }
],
'reward': 0
},
{
'state_transitions': [
{ 'state': 'top', 'action': 'sink', 'state_': 'top' },
{ 'state': 'top', 'action': 'climb', 'state_': 'top' },
],
'reward': 1
},
]
from learn import MarkovAgent
mark = MarkovAgent(observations + trap_states)
mark.learn()
print(mark.policy)
# {'high': 'climb', 'top': 'sink', 'bottom': 'sink', 'low': 'climb', 'mid': 'climb'}
# NOTE: policy in top and bottom states is chosen randomly (doesn't affect state)