68 lines
2.1 KiB
Python
68 lines
2.1 KiB
Python
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# mdp.py
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# ------
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# Licensing Information: You are free to use or extend these projects for
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# educational purposes provided that (1) you do not distribute or publish
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# solutions, (2) you retain this notice, and (3) you provide clear
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# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.
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#
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# Attribution Information: The Pacman AI projects were developed at UC Berkeley.
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# The core projects and autograders were primarily created by John DeNero
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# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).
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# Student side autograding was added by Brad Miller, Nick Hay, and
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# Pieter Abbeel (pabbeel@cs.berkeley.edu).
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import random
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class MarkovDecisionProcess:
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def getStates(self):
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"""
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Return a list of all states in the MDP.
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Not generally possible for large MDPs.
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"""
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abstract
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def getStartState(self):
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"""
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Return the start state of the MDP.
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"""
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abstract
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def getPossibleActions(self, state):
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"""
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Return list of possible actions from 'state'.
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"""
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abstract
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def getTransitionStatesAndProbs(self, state, action):
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"""
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Returns list of (nextState, prob) pairs
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representing the states reachable
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from 'state' by taking 'action' along
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with their transition probabilities.
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Note that in Q-Learning and reinforcment
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learning in general, we do not know these
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probabilities nor do we directly model them.
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"""
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abstract
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def getReward(self, state, action, nextState):
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"""
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Get the reward for the state, action, nextState transition.
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Not available in reinforcement learning.
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"""
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abstract
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def isTerminal(self, state):
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"""
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Returns true if the current state is a terminal state. By convention,
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a terminal state has zero future rewards. Sometimes the terminal state(s)
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may have no possible actions. It is also common to think of the terminal
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state as having a self-loop action 'pass' with zero reward; the formulations
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are equivalent.
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"""
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abstract
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