def env_init(self):
"""
Based on the levin model, the dispersion probability is initialized.
"""
self.dispersionModel = InvasiveUtility.Levin
notDirectedG = networkx.Graph(self.simulationParameterObj.graph)
adjMatrix = adjacency_matrix(notDirectedG)
edges = self.simulationParameterObj.graph.edges()
simulationParameterObj = self.simulationParameterObj
if self.dispersionModel == InvasiveUtility.Levin:
parameters = InvasiveUtility.calculatePath(notDirectedG,adjMatrix, edges, simulationParameterObj.downStreamRate,
simulationParameterObj.upStreamRate)
C = (1 - simulationParameterObj.upStreamRate * simulationParameterObj.downStreamRate) / (
(1 - 2 * simulationParameterObj.upStreamRate) * (1 - simulationParameterObj.downStreamRate))
self.dispertionTable = np.dot(1 / C, parameters)
self.germinationObj = GerminationDispersionParameterClass(1, 1)
#calculating the worst case fully invaded rivers cost
worst_case = repmat(1, 1, self.simulationParameterObj.nbrReaches * self.simulationParameterObj.habitatSize)[0]
cost_state_unit = InvasiveUtility.get_unit_invaded_reaches(worst_case,
self.simulationParameterObj.habitatSize) * self.actionParameterObj.costPerReach
stateCost = cost_state_unit + InvasiveUtility.get_invaded_reaches(
worst_case) * self.actionParameterObj.costPerTree
stateCost = stateCost + InvasiveUtility.get_empty_slots(worst_case) * self.actionParameterObj.emptyCost
costAction = InvasiveUtility.get_budget_cost_actions(repmat(3, 1, self.simulationParameterObj.nbrReaches)[0],
worst_case, self.actionParameterObj)
networkx.adjacency_matrix(self.simulationParameterObj.graph)
return "VERSION RL-Glue-3.0 PROBLEMTYPE non-episodic DISCOUNTFACTOR " + str(
self.discountFactor) + " OBSERVATIONS INTS (" + str(
self.simulationParameterObj.nbrReaches * self.simulationParameterObj.habitatSize) + " 1 3) ACTIONS INTS (" + str(
self.simulationParameterObj.nbrReaches) + " 1 4) REWARDS (" + str(self.Bad_Action_Penalty)+" "+str(
-1 * (costAction + stateCost)) + ") EXTRA "+str(self.simulationParameterObj.graph.edges()) + " BUDGET "+str(self.actionParameterObj.budget) +" by Majid Taleghan."
def env_init(self):
"""
Based on the levin model, the dispersion probability is initialized.
"""
self.dispersionModel = InvasiveUtility.Levin
notDirectedG = networkx.Graph(self.simulationParameterObj.graph)
adjMatrix = adjacency_matrix(notDirectedG)
edges = self.simulationParameterObj.graph.edges()
simulationParameterObj = self.simulationParameterObj
if self.dispersionModel == InvasiveUtility.Levin:
parameters = InvasiveUtility.calculatePath(notDirectedG,adjMatrix, edges, simulationParameterObj.downStreamRate,
simulationParameterObj.upStreamRate)
C = (1 - simulationParameterObj.upStreamRate * simulationParameterObj.downStreamRate) / (
(1 - 2 * simulationParameterObj.upStreamRate) * (1 - simulationParameterObj.downStreamRate))
self.dispertionTable = np.dot(1 / C, parameters)
self.germinationObj = GerminationDispersionParameterClass(1, 1)
#calculating the worst case fully invaded rivers cost
worst_case = repmat(1, 1, self.simulationParameterObj.nbrReaches * self.simulationParameterObj.habitatSize)[0]
cost_state_unit = InvasiveUtility.get_unit_invaded_reaches(worst_case,
self.simulationParameterObj.habitatSize) * self.actionParameterObj.costPerReach
stateCost = cost_state_unit + InvasiveUtility.get_invaded_reaches(
worst_case) * self.actionParameterObj.costPerTree
stateCost = stateCost + InvasiveUtility.get_empty_slots(worst_case) * self.actionParameterObj.emptyCost
costAction = InvasiveUtility.get_budget_cost_actions(repmat(3, 1, self.simulationParameterObj.nbrReaches)[0],
worst_case, self.actionParameterObj)
networkx.adjacency_matrix(self.simulationParameterObj.graph)
return "VERSION RL-Glue-3.0 PROBLEMTYPE non-episodic DISCOUNTFACTOR " + str(
self.discountFactor) + " OBSERVATIONS INTS (" + str(
self.simulationParameterObj.nbrReaches * self.simulationParameterObj.habitatSize) + " 1 3) ACTIONS INTS (" + str(
self.simulationParameterObj.nbrReaches) + " 1 4) REWARDS (" + str(self.Bad_Action_Penalty)+" "+str(
-1 * (costAction + stateCost)) + ") EXTRA "+str(self.simulationParameterObj.graph.edges()) + " BUDGET "+str(self.actionParameterObj.budget) +" by Majid Taleghan."
def env_step(self, action):
action = action.intArray
if len(action) != 3:
print action, len(action)
assert len(action) == self.simulationParameterObj.nbrReaches, "Expected " + str(
self.simulationParameterObj.nbrReaches) + " integer action."
if not InvasiveUtility.is_action_allowable(action, self.state):
theObs = Observation()
InvasiveUtility.is_action_allowable(action, self.state)
#map(int, results)
theObs.intArray = [-1]
returnRO = Reward_observation_terminal()
returnRO.r = self.Bad_Action_Penalty
returnRO.o = theObs
return returnRO
cost_state_unit = InvasiveUtility.get_unit_invaded_reaches(self.state,
self.simulationParameterObj.habitatSize) * self.actionParameterObj.costPerReach
stateCost = cost_state_unit + InvasiveUtility.get_invaded_reaches(
self.state) * self.actionParameterObj.costPerTree
stateCost = stateCost + InvasiveUtility.get_empty_slots(self.state) * self.actionParameterObj.emptyCost
costAction = InvasiveUtility.get_budget_cost_actions(action, self.state, self.actionParameterObj)
if costAction > self.actionParameterObj.budget:
theObs = Observation()
InvasiveUtility.is_action_allowable(action, self.state)
#map(int, results)
theObs.intArray = [-1]
returnRO = Reward_observation_terminal()
returnRO.r = self.Bad_Action_Penalty
returnRO.o = theObs
return returnRO
nextState = simulateNextState(self.state, action, self.simulationParameterObj,
self.actionParameterObj, self.dispertionTable, self.germinationObj)
self.state = nextState
theObs = Observation()
theObs.intArray = self.state
returnRO = Reward_observation_terminal()
returnRO.r = -1 * (costAction + stateCost)
returnRO.o = theObs
return returnRO
def env_step(self, action):
action = action.intArray
assert len(action) == self.simulationParameterObj.nbrReaches, "Expected " + str(
self.simulationParameterObj.nbrReaches) + " integer action."
if not InvasiveUtility.is_action_allowable(action, self.state):
theObs = Observation()
InvasiveUtility.is_action_allowable(action, self.state)
#map(int, results)
theObs.intArray = [-1]
returnRO = Reward_observation_terminal()
returnRO.r = self.Bad_Action_Penalty
returnRO.o = theObs
return returnRO
cost_state_unit = InvasiveUtility.get_unit_invaded_reaches(self.state,
self.simulationParameterObj.habitatSize) * self.actionParameterObj.costPerReach
stateCost = cost_state_unit + InvasiveUtility.get_invaded_reaches(
self.state) * self.actionParameterObj.costPerTree
stateCost = stateCost + InvasiveUtility.get_empty_slots(self.state) * self.actionParameterObj.emptyCost
costAction = InvasiveUtility.get_budget_cost_actions(action, self.state, self.actionParameterObj)
if costAction > self.actionParameterObj.budget:
theObs = Observation()
InvasiveUtility.is_action_allowable(action, self.state)
#map(int, results)
theObs.intArray = [-1]
returnRO = Reward_observation_terminal()
returnRO.r = self.Bad_Action_Penalty
returnRO.o = theObs
return returnRO
nextState = simulateNextState(self.state, action, self.simulationParameterObj,
self.actionParameterObj, self.dispertionTable, self.germinationObj)
self.state = nextState
theObs = Observation()
theObs.intArray = self.state
returnRO = Reward_observation_terminal()
returnRO.r = -1 * (costAction + stateCost)
returnRO.o = theObs
return returnRO
