mdp.py

import abstract


# ABSTRACT CLASS FOR GENERAL MDP'S

class MarkovDecisionProcess:

    def getStates(self):
        """
    Return a list of all states in the MDP.
    Not generally possible for large MDPs.
    """
        abstract

    def getStartState(self):
        """
    Return the start state of the MDP.
    """
        abstract

    def getPossibleActions(self, state):
        """
    Return list of possible actions from 'state'.
    """
        abstract

    def getTransitionStatesAndProbs(self, state, action):
        """
    Returns list of (nextState, prob) pairs
    representing the states reachable
    from 'state' by taking 'action' along
    with their transition probabilities.  
    
    Not available in reinforcement learning.
    """
        abstract

    def getReward(self, state, action, nextState):
        """
    Get the reward for the state, action, nextState transition.
    
    Not available in reinforcement learning.
    """
        abstract

    def isTerminal(self, state):
        """
    Returns true if the current state is a terminal state.  By convention,
    a terminal state has zero future rewards.  Sometimes the terminal state(s)
    may have no possible actions.  It is also common to think of the terminal
    state as having a self-loop action 'pass' with zero reward; the formulations
    are equivalent.
    """
        abstract