def run(args):
env = gym.make(args.env_name)
observation = env.reset()
steps_per_episode = args.steps
model_file_output_path = args.from_file
should_recycle_population = args.recycle_population
# N is episode steps length; D_in is input observation dimension;
# H is hidden layer dimension; D_out is output action space dimension.
N, D_in, H, D_out = args.batch_size, observation.shape[0], 40, 1
agent = Agent(env, steps_per_episode, args.maximize, args.reward_reducer)
model = Net(N, D_in, H, D_out, agent)
# set log level
agent.log_level = args.log_level
if args.load_model:
model.load_state_dict(torch.load(model_file_output_path))
model.eval()
# connect network to agent
agent.attach_model(model)
if args.load_model:
agent.run_forever(steps_per_episode)
exit()
# fill
model.train()
scaling_factor = args.scaling_factor
crossover_rate = args.crossover_rate
population_size = args.population_size
episodes_num = args.episodes # number of episodes
# generate flattened weights
model.flatten()
problem_size = model.flattened.shape[0]
print("problem_size: ", problem_size)
# Initial population, Fitness values
x = torch.randn(population_size, problem_size, dtype=torch.float64)
y = torch.randn(population_size, D_out, dtype=torch.float64)
# Convert to c pointers
x_c = x.detach().numpy().ctypes.data_as(c.POINTER(
c.c_double)) # c pointer init population
y_c = y.detach().numpy().ctypes.data_as(c.POINTER(
c.c_double)) # c pointer init fitness values
agent.out_population = x.detach().numpy()
agent.out_fitnesses = y.detach().numpy()
# TODO: make these adjustable
optimizer = getattr(devo, args.optimizer_name)
generations = episodes_num // population_size
# Runs 1 generation of DE at a time, using previous run's out population
# as an input for the next one
if should_recycle_population:
for g in range(generations):
# # Using Adaptive-DEs
optimizer.run(
population_size,
population_size, # population size
scaling_factor, # scaling factor
crossover_rate, # crossover rate
agent.objective_func,
problem_size, # problem size
-100, # unused value
100, # unused value
x_c,
y_c,
agent.results_callback # no results callback needed
)
x_c = agent.out_population.ctypes.data_as(c.POINTER(c.c_double))
y_c = agent.out_fitnesses.ctypes.data_as(c.POINTER(c.c_double))
else:
# # Using Adaptive-DEs
optimizer.run(
episodes_num,
population_size, # population size
scaling_factor, # scaling factor
crossover_rate, # crossover rate
agent.objective_func,
problem_size, # problem size
-100, # unused value
100, # unused value
x_c,
y_c,
agent.results_callback # no results callback needed
)
# Get mins - inverted in output
print("min_fitness: ", agent.min_reward)
model.update_weights_from_vec(agent.min_weights)
result = -agent.run_episode(agent.steps_per_episode, True)
if args.should_test:
print("test_run(expected: {}, actual: {})".format(
agent.min_reward, result))
env.close()
if args.save_model:
print("model_file: ", model_file_output_path)
# save model
torch.save(model.state_dict(), model_file_output_path)
