class ArrowConstructionTransformTrainer(ArrowConstructionTransform):
def __init__(self):
# Initialize super.
ArrowConstructionTransform.__init__(self)
# Define training world.
self.training_world = World(1, 1)
self.reset_training_world()
# Learn the Q-table.
def learn_policy(self):
# Initialize Q-learner.
qlearner = QLearner( \
self.state_space,
self.actions,
self.handle_action,
self.reset_training_world )
# Initialize reward states.
goal_states = [ \
( 0, self.state_space[1].index(World.ArrowState.Arrows_Complete) ),
( 1, self.state_space[1].index(World.ArrowState.Arrows_Complete) ) ]
for goal_state in goal_states:
qlearner.set_r_value( goal_state, 100 )
#print qlearner.r_table
# Run Q-learner.
qlearner.execute(goal_states, 300, 30)
# Return policy.
return qlearner.get_policy()
# Expands the reduced state into the training world.
def expand_training_state(self, reduced_state):
# Set world state based on reduced state.
expanded_state = World.State.from_state(self.training_world.agent_state)
expanded_state.arrows = self.state_space[1][reduced_state[1]]
expanded_state.minerals = expanded_state._world.needed_minerals if self.state_space[0][reduced_state[0]] else 0
expanded_state.bamboo = expanded_state._world.needed_bamboo if self.state_space[0][reduced_state[0]] else 0
return expanded_state
# Resets the training world.
def reset_training_world(self):
self.training_world.world_state[0][0] = World.SiteState.Useless
# Handles an action.
def handle_action(self, reduced_state, action_index):
action = self.actions[action_index]
expanded_state = self.expand_training_state(reduced_state)
#print " -- Reduced state : " + str(reduced_state)
#print " -- Expanded state: " + str(expanded_state)
expanded_state = self.training_world.perform_action(expanded_state, action)
#print " -- Expanded state: " + str(expanded_state)
new_reduced_state = self.reduce_state(expanded_state)
#print " -- Reduced state : " + str(new_reduced_state)
return new_reduced_state
class BambooTransformTrainer(BambooTransform):
def __init__(self):
# Initialize super.
BambooTransform.__init__(self)
# Define training world.
self.training_world = World(1, 1)
self.reset_training_world()
# Learn the Q-table.
def learn_policy(self):
# Initialize Q-learner.
qlearner = QLearner( \
self.state_space,
self.actions,
self.handle_action,
self.reset_training_world )
# Initialize reward states.
goal_states = [ \
( self.state_space[0].index(World.SiteState.Useless), ),
( self.state_space[0].index(World.SiteState.Bamboo_Planted), ) ]
for goal_state in goal_states:
qlearner.set_r_value( goal_state, 100 )
#print qlearner.r_table
# Run Q-learner.
qlearner.execute(goal_states, 250, 15)
# Return policy.
return qlearner.get_policy()
# Expands the reduced state into the training world.
def expand_training_state(self, reduced_state):
# Set world state based on reduced state.
expanded_state = World.State.from_state(self.training_world.agent_state)
self.training_world.world_state[0][0] = self.state_space[0][reduced_state[0]]
return expanded_state
# Resets the training world.
def reset_training_world(self):
self.training_world.world_state[0][0] = World.SiteState.Useless
# Handles an action.
def handle_action(self, reduced_state, action_index):
action = self.actions[action_index]
expanded_state = self.expand_training_state(reduced_state)
#print " -- Reduced state : " + str(reduced_state)
#print " -- Expanded state: " + str(expanded_state)
expanded_state = self.training_world.perform_action(expanded_state, action)
#print " -- Expanded state: " + str(expanded_state)
new_reduced_state = self.reduce_state(expanded_state)
#print " -- Reduced state : " + str(new_reduced_state)
return new_reduced_state
class PositionTransformTrainer(PositionTransform):
def __init__(self):
# Initialize super.
PositionTransform.__init__(self)
# Define training world.
self.training_world = World(3, 3)
self.training_world.world_state[PositionTransform.VerticleState.At + 1][
PositionTransform.HorizontalState.At + 1
] = World.SiteState.Mineral_Deposit
# Learn the Q-table.
def learn_policy(self):
# Initialize Q-learner.
qlearner = QLearner(self.state_space, self.actions, self.handle_action, self.reset_training_world)
# Initialize reward states.
goal_states = [(PositionTransform.HorizontalState.At + 1, PositionTransform.VerticleState.At + 1)]
for goal_state in goal_states:
qlearner.set_r_value(goal_state, 100)
# print qlearner.r_table
# Run Q-learner.
qlearner.execute(goal_states, 300, 50)
# Return policy.
return qlearner.get_policy()
# Expands the reduced state into the training world.
def expand_training_state(self, reduced_state):
# Set world state based on reduced state.
expanded_state = World.State.from_state(self.training_world.agent_state)
expanded_state.x = reduced_state[0]
expanded_state.y = reduced_state[1]
return expanded_state
# Resets the training world.
def reset_training_world(self):
# Training world does not require a reset in this transformation.
pass
# Handles an action.
def handle_action(self, reduced_state, action_index):
action = self.actions[action_index]
expanded_state = self.expand_training_state(reduced_state)
# print " -- Reduced state : " + str(reduced_state)
# print " -- Expanded state: " + str(expanded_state)
expanded_state = self.training_world.perform_action(expanded_state, action)
# print " -- Expanded state: " + str(expanded_state)
new_reduced_state = self.reduce_state(expanded_state)
# print " -- Reduced state : " + str(new_reduced_state)
return new_reduced_state
class FullTransformTrainer(FullTransform):
def __init__(self, cell_width, cell_height):
# Initialize super.
FullTransform.__init__(self, cell_width, cell_height)
# Define training world.
self.training_world = World(cell_width, cell_height)
self.reset_training_world()
# Learn the Q-table.
def learn_policy(self):
# Initialize Q-learner.
qlearner = QLearner( \
self.state_space,
self.actions,
self.handle_action,
self.reset_training_world )
# Initialize goal states.
goal_states = []
print "Enumerating goal states..."
print self.state_space_dim
for state_index in xrange(qlearner.r_table.size):
state = numpy.unravel_index(state_index, qlearner.r_table.shape)
if state[FullTransform.StateOffset.Arrows] == World.ArrowState.Arrows_Complete:
goal_states.append(tuple(state))
print "Goal states: %d" % len(goal_states)
for goal_state in goal_states:
qlearner.set_r_value( goal_state, 100 )
#print qlearner.r_table
# Run Q-learner.
print "Total states: %d" % (qlearner.r_table.size)
qlearner.execute(goal_states, 500000, 50)
# Return policy.
return qlearner.get_policy()
# Expands the reduced state into the training world.
def expand_training_state(self, reduced_state):
# Set world state based on reduced state.
expanded_state = World.State.from_state(self.training_world.agent_state)
world = expanded_state._world
# Set world state.
for index in xrange(self.world_size):
(x, y) = numpy.unravel_index(index, self.world_shape)
world.world_state[y][x]
# Set other elements.
expanded_state.x = reduced_state[FullTransform.StateOffset.X]
expanded_state.y = reduced_state[FullTransform.StateOffset.Y]
expanded_state.minerals = 50 * reduced_state[FullTransform.StateOffset.Minerals]
expanded_state.bamboo = 50 * reduced_state[FullTransform.StateOffset.Bamboo]
expanded_state.arrows = reduced_state[FullTransform.StateOffset.Arrows]
return expanded_state
# Resets the training world.
def reset_training_world(self):
pass
# Handles an action.
def handle_action(self, reduced_state, action_index):
action = self.actions[action_index]
expanded_state = self.expand_training_state(reduced_state)
expanded_state = self.training_world.perform_action(expanded_state, action)
new_reduced_state = self.reduce_state(expanded_state)
return new_reduced_state