def runMethods(idx_state, methods, Environment, heuristicFn_nnet, args, data):
idx, state = idx_state
stateStr = " ".join([str(x) for x in state])
# print(stateStr)
for method in methods:
start_time = time.time()
if method == "kociemba":
soln = Kociemba.solve(state)
nodesGenerated_num = 0
elapsedTime = time.time() - start_time
elif method == "nnet":
BestFS_solve = search_utils.BestFS_solve([state], heuristicFn_nnet, Environment, bfs=args.bfs)
isSolved, solveSteps, nodesGenerated_num = BestFS_solve.run(numParallel=args.nnet_parallel,
depthPenalty=args.depth_penalty,
verbose=args.verbose)
BestFS_solve = []
del BestFS_solve
gc.collect()
soln = solveSteps[0]
nodesGenerated_num = nodesGenerated_num[0]
elapsedTime = time.time() - start_time
else:
continue
data["times"][method][idx] = elapsedTime
data["nodesGenerated_num"][method][idx] = nodesGenerated_num
assert (validSoln(state, soln, Environment))
data["solutions"][method][idx] = soln
def solveNnet(self):
startTime = time.time()
BestFS_solve = search_utils.BestFS_solve(self.state,self.heuristicFn,self.env,bfs=0)
isSolved, solveSteps, nodesGenerated_num = BestFS_solve.run(numParallel=100,depthPenalty=0.1,verbose=True)
### Make move
moves = solveSteps[0]
print("Neural network found solution of length %i (%s)" % (len(moves),time.time()-startTime))
for move in moves:
self.state = self.env.next_state(self.state,move)
self._updatePlot()
time.sleep(0.5)
def runMethods(state):
stateStr = " ".join([str(x) for x in state])
# print(stateStr)
start_time = time.time()
runType = "python"
# if (args.env.upper() in ['CUBE3','PUZZLE15','PUZZLE24','PUZZLE35']) or ('LIGHTSOUT' in args.env.upper()):
# runType = "cpp"
# else:
# runType = "python"
if runType == "cpp":
popen = Popen(
['./ml_utils/parallel_weighted_astar', stateStr, str(0.2), str(100),
socketName, 'cube3'], stdout=PIPE, stderr=PIPE, bufsize=1, universal_newlines=True)
lines = []
for stdout_line in iter(popen.stdout.readline, ""):
stdout_line = stdout_line.strip('\n')
lines.append(stdout_line)
# if args.verbose:
# sys.stdout.write("%s\n" % (stdout_line))
# sys.stdout.flush()
moves = [int(x) for x in lines[-3].split(" ")[:-1]]
soln = [Environment.legalPlays[x] for x in moves][::-1]
nodesGenerated_num = int(lines[-1])
else:
BestFS_solve = search_utils.BestFS_solve([state], heuristicFn_nnet, Environment, 0)
isSolved, solveSteps, nodesGenerated_num = BestFS_solve.run(numParallel=100,
depthPenalty=0.2,
verbose=False)
BestFS_solve = []
del BestFS_solve
gc.collect()
soln = solveSteps[0]
nodesGenerated_num = nodesGenerated_num[0]
elapsedTime = time.time() - start_time
assert (validSoln(state, soln, Environment))
return [state, soln]
Environment,
maxTurns=maxTurns,
searchDepth=searchDepth,
numRollouts=numRollouts,
searchMethod=solveMethod,
verbose=verbose)
percentSolved = 100 * float(sum(isSolved)) / float(len(isSolved))
avgSolveSteps = 0.0
if percentSolved > 0.0:
avgSolveSteps = np.mean(solveSteps[isSolved])
elif solveMethod == "BESTFS":
stateVals = heuristicFn(np.stack(testStates_cube, axis=0))
BestFS_solve = search_utils.BestFS_solve(testStates_cube, heuristicFn,
Environment)
isSolved, solveSteps = BestFS_solve.run(
numParallel=numRollouts,
depthPenalty=args.depth_penalty,
verbose=verbose)
percentSolved = 100 * float(sum(isSolved)) / float(len(isSolved))
avgSolveSteps = 0.0
if percentSolved > 0.0:
solveSteps_isSolved = [
x for x, y in zip(solveSteps, isSolved) if y == True
]
avgSolveSteps = np.mean([len(x) for x in solveSteps_isSolved])
solve_elapsed_time = time.time() - solve_start_time
print(
def runMethods(idx_state):
idx, state = idx_state
stateStr = " ".join([str(x) for x in state])
#print(stateStr)
for method in methods:
start_time = time.time()
if method == "kociemba":
soln = Kociemba.solve(state)
nodesGenerated_num = 0
elapsedTime = time.time() - start_time
elif method == "nnet":
runType = "python"
#if (args.env.upper() in ['CUBE3','PUZZLE15','PUZZLE24','PUZZLE35']) or ('LIGHTSOUT' in args.env.upper()):
# runType = "cpp"
#else:
# runType = "python"
if runType == "cpp":
popen = Popen([
'./ml_utils/parallel_weighted_astar', stateStr,
str(args.depth_penalty),
str(args.nnet_parallel), socketName, args.env
],
stdout=PIPE,
stderr=PIPE,
bufsize=1,
universal_newlines=True)
lines = []
for stdout_line in iter(popen.stdout.readline, ""):
stdout_line = stdout_line.strip('\n')
lines.append(stdout_line)
if args.verbose:
sys.stdout.write("%s\n" % (stdout_line))
sys.stdout.flush()
moves = [int(x) for x in lines[-3].split(" ")[:-1]]
soln = [Environment.legalPlays[x] for x in moves][::-1]
nodesGenerated_num = int(lines[-1])
else:
BestFS_solve = search_utils.BestFS_solve([state],
heuristicFn_nnet,
Environment,
bfs=args.bfs)
isSolved, solveSteps, nodesGenerated_num = BestFS_solve.run(
numParallel=args.nnet_parallel,
depthPenalty=args.depth_penalty,
verbose=args.verbose)
BestFS_solve = []
del BestFS_solve
gc.collect()
soln = solveSteps[0]
nodesGenerated_num = nodesGenerated_num[0]
elapsedTime = time.time() - start_time
elif method == "optimal":
soln, nodesGenerated_num, elapsedTime = Optimal.solve(state)
else:
continue
data["times"][method][idx] = elapsedTime
data["nodesGenerated_num"][method][idx] = nodesGenerated_num
assert (validSoln(state, soln, Environment))
data["solutions"][method][idx] = soln
def getResult(states):
nnet_parallel = 100
depth_penalty = 0.2
bfs = 0
startIdx = 0
endIdx = -1
verbose = False
useGPU = False
if endIdx == -1:
endIdx = len(states)
states = states[startIdx:endIdx]
print('state:', states)
### Load nnet
from ml_utils import nnet_utils
from ml_utils import search_utils
if len(os.environ['CUDA_VISIBLE_DEVICES']) > 1:
gpuNums = [
int(x) for x in os.environ['CUDA_VISIBLE_DEVICES'].split(",")
]
else:
gpuNums = [None]
numParallel = len(gpuNums)
### Initialize files
dataQueues = []
resQueues = []
for num in range(numParallel):
dataQueues.append(Queue(1))
resQueues.append(Queue(1))
dataListenerProc = Process(target=dataListener,
args=(
dataQueues[num],
resQueues[num],
gpuNums[num],
))
dataListenerProc.daemon = True
dataListenerProc.start()
def heuristicFn_nnet(x):
### Write data
parallelNums = range(min(numParallel, x.shape[0]))
splitIdxs = np.array_split(np.arange(x.shape[0]), len(parallelNums))
for num in parallelNums:
dataQueues[num].put(x[splitIdxs[num]])
### Check until all data is obtaied
results = [None] * len(parallelNums)
for num in parallelNums:
results[num] = resQueues[num].get()
results = np.concatenate(results)
return (results)
### Get solutions
BestFS_solve = search_utils.BestFS_solve([states],
heuristicFn_nnet,
Environment,
bfs=bfs)
isSolved, solveSteps, nodesGenerated_num = BestFS_solve.run(
numParallel=nnet_parallel, depthPenalty=depth_penalty, verbose=verbose)
BestFS_solve = []
del BestFS_solve
gc.collect()
soln = solveSteps[0]
return soln
