def main():
ap_map = {'a': (2, 2), 'b': (6, 3), 'c': (5, 3), 'd': (4, 2)}
print('Automic propositions, ', ap_map)
ltlformula = 'F (b & Fa)'
print('LTL Formula, ', ltlformula)
# Setup MDP, Agents.
print('translatinggg')
a = spot.translate('(a U b) & GFc & GFd', 'BA', 'complete')
a.show("v" "")
return
mdp = LTLGridWorldMDP(ltltask=ltlformula,
ap_map=ap_map,
width=6,
height=6,
goal_locs=[(6, 6)],
slip_prob=0.2)
mdp.automata.subproblem_flag = 0
mdp.automata.subproblem_stay = 1
mdp.automata.subproblem_goal = 0
value_iter = ValueIteration(mdp, sample_rate=5)
value_iter.run_vi()
# Value Iteration.
print('Value iteration')
action_seq, state_seq = value_iter.plan(mdp.get_init_state())
print("Plan for", mdp)
for i in range(len(action_seq)):
print("\t", action_seq[i], state_seq[i])
def get_l1_policy(start_room=None,
goal_room=None,
mdp=None,
starting_items=None,
goal_items=None,
actions=None,
doors=None,
rooms=None):
if mdp is None:
mdp = FourRoomL1MDP(start_room,
goal_room,
starting_items=starting_items,
goal_items=goal_items,
actions=actions,
doors=doors,
rooms=rooms)
vi = ValueIteration(mdp)
vi.run_vi()
policy = defaultdict()
action_seq, state_seq = vi.plan(mdp.init_state)
print 'Plan for {}:'.format(mdp)
for i in range(len(action_seq)):
print "\tpi[{}] -> {}".format(state_seq[i], action_seq[i])
policy[state_seq[i]] = action_seq[i]
return policy
def main():
ap_map = {'a': (2, 2), 'b': (6, 3), 'c': (5, 3), 'd': (4, 2)}
ltlformula = 'F (b & Fa)'
# Setup MDP, Agents.
mdp = LTLGridWorldMDP(ltltask=ltlformula,
ap_map=ap_map,
width=6,
height=6,
goal_locs=[(6, 6)],
slip_prob=0.2)
mdp.automata.subproblem_flag = 0
mdp.automata.subproblem_stay = 1
mdp.automata.subproblem_goal = 0
value_iter = ValueIteration(mdp, sample_rate=5)
value_iter.run_vi()
# Value Iteration.
action_seq, state_seq = value_iter.plan(mdp.get_init_state())
print("Plan for", mdp)
for i in range(len(action_seq)):
print("\t", action_seq[i], state_seq[i])
print(ltlformula)
f = open('/Users/romapatel/Desktop/actions.tsv', 'w+')
for item in state_seq:
f.write(str(item) + '\n')
f.close()
model = None
ltl_visualiser(model)
def main():
# Setup MDP, Agents.
mdp = GridWorldMDP(width=6, height=6, goal_locs=[(6, 6)], slip_prob=0.2)
value_iter = ValueIteration(mdp, sample_rate=5)
value_iter.run_vi()
# Value Iteration.
action_seq, state_seq = value_iter.plan(mdp.get_init_state())
print("Plan for", mdp)
for i in range(len(action_seq)):
print("\t", action_seq[i], state_seq[i])
def get_l1_policy(domain):
vi = ValueIteration(domain, sample_rate=1)
vi.run_vi()
policy = defaultdict()
action_seq, state_seq = vi.plan(domain.init_state)
print('Plan for {}:'.format(domain))
for i in range(len(action_seq)):
print("\tpi[{}] -> {}\n".format(state_seq[i], action_seq[i]))
policy[state_seq[i]] = action_seq[i]
return policy
def get_l1_policy(start_room=None, goal_room=None, mdp=None):
if mdp is None:
mdp = CubeL1MDP(start_room, goal_room)
vi = ValueIteration(mdp)
vi.run_vi()
policy = defaultdict()
action_seq, state_seq = vi.plan(mdp.init_state)
print('Plan for {}:'.format(mdp))
for i in range(len(action_seq)):
print("\tpi[{}] -> {}".format(state_seq[i], action_seq[i]))
policy[state_seq[i]] = action_seq[i]
return policy
def main():
# Setup MDP, Agents.
mdp = GridWorldMDP(width=6, height=6, goal_locs=[(6, 6)], slip_prob=0.2)
value_iter = ValueIteration(mdp, sample_rate=5)
mcts = MCTS(mdp, num_rollouts_per_step=50)
# _, val = value_iter.run_vi()
# Value Iteration.
vi_action_seq, vi_state_seq = value_iter.plan(mdp.get_init_state())
mcts_action_seq, mcts_state_seq = mcts.plan(mdp.get_init_state())
print("Plan for", mdp)
for i in range(len(mcts_action_seq)):
print("\t", mcts_action_seq[i], mcts_state_seq[i])
def get_l1_policy(oomdp=None):
if oomdp is None:
oomdp = TaxiL1OOMDP()
vi = ValueIteration(oomdp, sample_rate=1)
vi.run_vi()
policy = defaultdict()
action_seq, state_seq = vi.plan(oomdp.init_state)
print('Plan for {}:'.format(oomdp))
for i in range(len(action_seq)):
print("\tpi[{}] -> {}\n".format(state_seq[i], action_seq[i]))
policy[state_seq[i]] = action_seq[i]
return policy
def main():
# Setup MDP, Agents.
mdp = GridWorldMDP(width=6, height=6, goal_locs=[(6, 6)], slip_prob=0.2)
value_iter = ValueIteration(mdp, sample_rate=5)
mcts = MCTS(mdp, num_rollouts_per_step=50)
# _, val = value_iter.run_vi()
# Value Iteration.
vi_action_seq, vi_state_seq = value_iter.plan(mdp.get_init_state())
mcts_action_seq, mcts_state_seq = mcts.plan(mdp.get_init_state())
print("Plan for", mdp)
for i in range(len(mcts_action_seq)):
print("\t", mcts_action_seq[i], mcts_state_seq[i])
def main():
ap_map = {'a': (2,2),'b': (6,3), 'c': (5,3), 'd': (4,2)}
ltlformula = 'F (b & Fa)'
# Setup MDP, Agents.
mdp = LTLGridWorldMDP(ltltask=ltlformula, ap_map=ap_map, width=6, height=6, goal_locs=[(6, 6)], slip_prob=0.2)
mdp.automata.subproblem_flag = 0
mdp.automata.subproblem_stay = 1
mdp.automata.subproblem_goal = 0
value_iter = ValueIteration(mdp, sample_rate=5)
value_iter.run_vi()
# Value Iteration.
action_seq, state_seq = value_iter.plan(mdp.get_init_state())
print("Plan for", mdp)
for i in range(len(action_seq)):
print("\t", action_seq[i], state_seq[i])
def get_l1_policy(start_room=None, goal_room=None, mdp=None):
if mdp is None:
mdp = FourRoomL1MDP(start_room,
goal_room,
starting_items=[2, 0],
goal_items=[2,
1]) #room 2, light off =0, light on =1
vi = ValueIteration(mdp)
vi.run_vi()
policy = defaultdict()
action_seq, state_seq = vi.plan(mdp.init_state)
print 'Plan for {}:'.format(mdp)
for i in range(len(action_seq)):
print "\tpi[{}] -> {}".format(state_seq[i], action_seq[i])
policy[state_seq[i]] = action_seq[i]
return policy
def run_plain_pMDP(init_loc, ltl_formula, cube_env, ap_maps, verbose=False):
start_time = time.time()
mdp = RoomCubePlainMDP(init_loc=init_loc,
ltl_formula=ltl_formula,
env_file=[cube_env],
ap_maps=ap_maps)
value_iter = ValueIteration(mdp, sample_rate=1, max_iterations=50)
value_iter.run_vi()
# Value Iteration
action_seq, state_seq = value_iter.plan(mdp.get_init_state())
computing_time = time.time() - start_time
# Print
if verbose:
print("=====================================================")
print("Plain: Plan for ", ltl_formula)
for i in range(len(action_seq)):
room_number, floor_number = mdp._get_abstract_number(state_seq[i])
print("\t {} in room {} on the floor {}, {}".format(
state_seq[i], room_number, floor_number, action_seq[i]))
room_number, floor_number = mdp._get_abstract_number(state_seq[-1])
print("\t {} in room {} on the floor {}".format(
state_seq[-1], room_number, floor_number))
# success?
if len(state_seq) <= 1:
flag_success = -1
else:
if mdp.automata.aut_spot.state_is_accepting(state_seq[-1].q):
flag_success = 1
else:
flag_success = 0
return computing_time, len(
action_seq
), flag_success, state_seq, action_seq, value_iter.get_num_backups_in_recent_run(
)
#!/usr/bin/env python
'''
NOTE: Incomplete. Planning infrastructure in development.
'''
# Other imports.
import srl_example_setup
from simple_rl.tasks import GridWorldMDP
from simple_rl.planning import ValueIteration, MCTS
# Setup MDP, Agents.
mdp = GridWorldMDP(width=6, height=6, goal_locs=[(6, 6)])
vi = ValueIteration(mdp)
vi.run_vi()
action_seq, state_seq = vi.plan(mdp.get_init_state())
for i in range(len(action_seq)):
print action_seq[i], state_seq[i]
# ltl_formula = 'F (a&b)'
ap_maps = {'a': [1, 'state', 7], 'b': [2, 'state', 3]}
#'c': [1, 'state', 3], 'd': [0, 'state', (6, 1, 1)],
# 'e': [2, 'state', 1],
# 'f': [2, 'state', 3], 'g': [0, 'state', (3, 4, 1)]}
start_time = time.time()
mdp = RoomCubePlainMDP(ltl_formula=ltl_formula,
env_file=[cube_env1],
ap_maps=ap_maps)
value_iter = ValueIteration(mdp, sample_rate=1)
value_iter.run_vi()
# Value Iteration
action_seq, state_seq = value_iter.plan(mdp.get_init_state())
computing_time = time.time() - start_time
# print
print("Plan for", mdp)
for i in range(len(action_seq)):
room_number, floor_number = mdp._get_abstract_number(state_seq[i])
print("\t {} in room {} on the floor {}, {}".format(
state_seq[i], room_number, floor_number, action_seq[i]))
room_number, floor_number = mdp._get_abstract_number(state_seq[-1])
print("\t {} in room {} on the floor {}".format(state_seq[-1], room_number,
floor_number))
print("Summary")
