parser.add_argument('--rewards-net', help='filename containing a trained rewards network')
parser.add_argument('--walk-len', default=1000, help='number of steps to take in the world', type=int)
args = parser.parse_args()
if not args.type:
parser.print_help()
sys.exit(1)
if args.type == 'nnet':
if not args.transitions_net or not args.rewards_net:
parser.print_help()
sys.exit(1)
(tnet, tset) = io.read_neural_net(args.transitions_net)
(rnet, rset) = io.read_neural_net(args.rewards_net)
state_dim = tnet['in'].dim - 1
reward_dim = rnet['out'].dim
state = np.zeros(state_dim)
for i in range(args.walk_len/2):
action = npr.random() * 0.5 + 0.5
new_state = net.activation(tnet, state, action)
reward = net.activation(rnet, state, action)
entry = state.tolist() + [action] + new_state.tolist() + reward.tolist()
print('{}'.format(' '.join([str(x) for x in entry])))
state = new_state
for i in range(args.walk_len/2):
action = npr.random() * 0.5 - 0.5
new_state = net.activation(tnet, state, action)
# TODO: incorporate correlated rewards somehow
maze = grd.make_multimaze(args.rows, args.cols, args.tasks)
goals = grd.maze_goal_states(maze, args.tasks, args.rmeans, cov)
io.write_maze_instance(maze, goals)
print('# type={}, rows={}, cols={}, correlation={}, stdev={}'.
format(args.type, args.rows, args.cols, args.correlation.tolist(), args.stdev.tolist()))
# perturbation of existing instances
elif args.type == 'perturbation':
if not args.baseline:
parser.print_help()
sys.exit(1)
else:
# TODO: handle other kinds of models
(net, trainset) = io.read_neural_net(args.baseline)
net2 = net.fuzz_neural_net(net, args.fuzz_frac, args.fuzz_scale)
io.write_neural_net(net2, trainset, 'fuzzed.net')
io.write_train_log(net2, trainset, 'train_log_fuzzed.dat')
# for discrete istances, we need to generate transition graphs and reward structures
# and write them out directly
elif args.type == 'discrete':
if not args.graph_type:
parser.print_help()
sys.exit(1)
params = None
if args.graph_type == 'fern':
params = {'frond_probability': args.frond_probability,
