def main():
parameters = parse_mover_problem_parameters()
filename = 'pidx_%d_planner_seed_%d.pkl' % (parameters.pidx, parameters.planner_seed)
save_dir = make_and_get_save_dir(parameters, filename)
set_seed(parameters.pidx)
problem_env = PaPMoverEnv(parameters.pidx)
goal_object_names = [obj.GetName() for obj in problem_env.objects[:parameters.n_objs_pack]]
goal_region_name = [problem_env.regions['home_region'].name]
goal = goal_region_name + goal_object_names
problem_env.set_goal(goal)
goal_entities = goal_object_names + goal_region_name
if parameters.use_shaped_reward:
reward_function = ShapedRewardFunction(problem_env, goal_object_names, goal_region_name[0],
parameters.planning_horizon)
else:
reward_function = GenericRewardFunction(problem_env, goal_object_names, goal_region_name[0],
parameters.planning_horizon)
motion_planner = OperatorBaseMotionPlanner(problem_env, 'prm')
problem_env.set_reward_function(reward_function)
problem_env.set_motion_planner(motion_planner)
learned_q = None
prior_q = None
if parameters.use_learned_q:
learned_q = load_learned_q(parameters, problem_env)
v_fcn = lambda state: -len(get_objects_to_move(state, problem_env))
if parameters.planner == 'mcts':
planner = MCTS(parameters, problem_env, goal_entities, prior_q, learned_q)
elif parameters.planner == 'mcts_with_leaf_strategy':
planner = MCTSWithLeafStrategy(parameters, problem_env, goal_entities, v_fcn, learned_q)
else:
raise NotImplementedError
set_seed(parameters.planner_seed)
search_time_to_reward, plan = planner.search(max_time=parameters.timelimit)
pickle.dump({"search_time_to_reward": search_time_to_reward, 'plan': plan,
'n_nodes': len(planner.tree.get_discrete_nodes())}, open(save_dir+filename, 'wb'))
def main():
commit_hash = get_commit_hash()
parameters = parse_mover_problem_parameters()
filename = 'pidx_%d_planner_seed_%d.pkl' % (parameters.pidx,
parameters.planner_seed)
save_dir = make_and_get_save_dir(parameters, filename, commit_hash)
solution_file_name = save_dir + filename
is_problem_solved_before = os.path.isfile(solution_file_name)
print solution_file_name
if is_problem_solved_before and not parameters.f:
print "***************Already solved********************"
with open(solution_file_name, 'rb') as f:
trajectory = pickle.load(f)
tottime = trajectory['search_time_to_reward'][-1][2]
print 'Time: %.2f ' % tottime
sys.exit(-1)
set_seed(parameters.pidx)
problem_env = PaPMoverEnv(parameters.pidx)
goal_objs = [
'square_packing_box1', 'square_packing_box2',
'rectangular_packing_box3', 'rectangular_packing_box4'
]
goal_region = 'home_region'
problem_env.set_goal(goal_objs, goal_region)
goal_entities = goal_objs + [goal_region]
if parameters.use_shaped_reward:
# uses the reward shaping per Ng et al.
reward_function = ShapedRewardFunction(problem_env, goal_objs,
goal_region,
parameters.planning_horizon)
else:
reward_function = GenericRewardFunction(problem_env, goal_objs,
goal_region,
parameters.planning_horizon)
motion_planner = OperatorBaseMotionPlanner(problem_env, 'prm')
problem_env.set_reward_function(reward_function)
problem_env.set_motion_planner(motion_planner)
learned_q = None
prior_q = None
if parameters.use_learned_q:
learned_q = load_learned_q(parameters, problem_env)
v_fcn = lambda state: -len(get_objects_to_move(state, problem_env))
if parameters.planner == 'mcts':
planner = MCTS(parameters, problem_env, goal_entities, prior_q,
learned_q)
elif parameters.planner == 'mcts_with_leaf_strategy':
planner = MCTSWithLeafStrategy(parameters, problem_env, goal_entities,
v_fcn, learned_q)
else:
raise NotImplementedError
set_seed(parameters.planner_seed)
stime = time.time()
search_time_to_reward, n_feasibility_checks, plan = planner.search(
max_time=parameters.timelimit)
tottime = time.time() - stime
print 'Time: %.2f ' % (tottime)
# todo
# save the entire tree
pickle.dump(
{
"search_time_to_reward": search_time_to_reward,
'plan': plan,
'commit_hash': commit_hash,
'n_feasibility_checks': n_feasibility_checks,
'n_nodes': len(planner.tree.get_discrete_nodes())
}, open(save_dir + filename, 'wb'))