The number of succesful runs
"""
for run in range(num_runs):
fitness = eval_fitness(net, max_bal_steps=additional_steps)
if fitness < config.fitness_threshold:
return run
return num_runs
if __name__ == '__main__':
# Determine path to configuration file. This path manipulation is
# here so that the script will run successfully regardless of the
# current working directory.
config_path = os.path.join(local_dir, 'two_pole_markov_config.ini')
# Clean results of previous run if any or init the ouput directory
utils.clear_output(out_dir)
# Run the experiment
pole_length = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]
num_runs = len(pole_length)
for i in range(num_runs):
cart.LENGTH_2 = pole_length[i] / 2.0
solved = run_experiment(config_path, n_generations=100, silent=True)
print("run: %d, solved: %s, half-length: %f" %
(i + 1, solved, cart.LENGTH_2))
if solved:
print("Solution found in: %d run, short pole length: %f" %
(i + 1, pole_length[i]))
break
help='The width of the records subplot')
parser.add_argument('--height',
type=int,
default=400,
help='The height of the records subplot')
args = parser.parse_args()
if not (args.maze == 'medium' or args.maze == 'hard'):
print('Unsupported maze configuration: %s' % args.maze)
exit(1)
# Determine path to configuration file.
config_path = os.path.join(local_dir, 'maze_config.ini')
trial_out_dir = os.path.join(out_dir, args.maze)
# Clean results of previous run if any or init the ouput directory
utils.clear_output(trial_out_dir)
# Run the experiment
maze_env_config = os.path.join(local_dir, '%s_maze.txt' % args.maze)
maze_env = maze.read_environment(maze_env_config)
# visualize.draw_maze_records(maze_env, None, view=True)
print("Starting the %s maze experiment" % args.maze)
run_experiment(config_file=config_path,
maze_env=maze_env,
trial_out_dir=trial_out_dir,
n_generations=args.generations,
args=args)
