def run_controller(trials=10, epochs =250, timeslice=0.002, tmax=0.2, threshold =((-pi/2), (pi/2))):
#step 0: set up Neural network
#step 1: Start pendulum with random force
#step 2: at n milliseconds take state
#step 3: pass state to genetic algorithm
#step 4: GA runs n epochs and evaluates what gives the best output
#step 5: GA passes weights to NN to evaluate a force to apply to pendulum
#Step 6: NN applies force to pendulum
#Step 7: Goto step 2
#TODO run multiple simulations, collect avg data and error and produce graphs
MAX_REWARD = 1000
pendulum = InvertedPendulum()
NN = NEvoNetwork(inputs=6, outputs=1, hiddenlayers=1, hiddenneurons=12, inputweights=6, activation=TanhActivation)
ga = Population(NN=NN, size=30)
main_array = []
reward_array =[]
for trial in range(0, trials):
ga.create(size=30)
force = np.random.randint(-5, 5)
while force == 0:
force = np.random.randint(-5, 5)
initial_state = State()
initial_state.theta = pi
initial_state, time = pendulum.get_State(u=force, initialstate=initial_state, tmax=tmax, timeslice=timeslice)
print('Force={1:3f} -Theta={0:4f}'.format(initial_state[-1].theta, force))
reward_array = []
population_array = []
for epoch in range(0, epochs):
for induhvidual in ga.individuals:
NN.set_weights(induhvidual.alleles)
state = []
state.append(initial_state[-1])
airborne = True
while airborne:
force = NN.get_outputs([state[-1].x, state[-1].xdot, state[-1].x2dot, state[-1].theta, state[-1].thetadot, state[-1].theta2dot])[0] * 5
state, time = pendulum.get_State(u=force, initialstate=state[-1], tmax=tmax, timeslice=timeslice)
if state[-1].theta < threshold[0] or state[-1].theta > threshold[1]:
airborne = False
else:
induhvidual.set_fitness(1)
if induhvidual.get_fitness() >= MAX_REWARD: break
reward_array.append(ga.getFittestIndividual().get_fitness())
population_array.append(ga.getPopulationFitness())
print('Trial: {0} - Epoch {1} --> Best fitness score = {2}, - Pop Fitness = {3}'
.format(trial, epoch, ga.getFittestIndividual().get_fitness(), ga.getPopulationFitness()))
ga.evolve(epochs=1)
main_array.append((reward_array, population_array))
return main_array
