default=0.05,
type=float,
help="Learning rate.")
parser.add_argument("--alpha_final",
default=0.001,
type=float,
help="Final learning rate.")
parser.add_argument("--evaluate",
default=False,
type=bool,
help="Run evaluation phase.")
args = parser.parse_args()
# Create the environment
env = car_racing_evaluator.environment()
# Construct the network
discrete_steer = [-1, 0, 1]
discrete_gas = [0, 1]
discrete_brake = [0, 1]
discretized_actions = np.array([
x for x in itertools.product(discrete_steer, discrete_gas,
discrete_brake)
])
action_size = len(discretized_actions)
network = Network()
network.construct(args, env.state_shape, action_size)
# Training
for _ in range(args.episodes // args.batch_size):
type=float,
help="Final exploration factor.")
parser.add_argument("--gamma",
default=None,
type=float,
help="Discounting factor.")
args = parser.parse_args()
# Fix random seeds and number of threads
np.random.seed(42)
tf.random.set_seed(42)
tf.config.threading.set_inter_op_parallelism_threads(args.threads)
tf.config.threading.set_intra_op_parallelism_threads(args.threads)
# Create the environment
env = car_racing_evaluator.environment(args.frame_skip)
# TODO: Implement a variation to Deep Q Network algorithm.
#
# Example: How to perform an episode with "always gas" agent.
state, done = env.reset(), False
while not done:
if args.render_each and (env.episode + 1) % args.render_each == 0:
env.render()
action = [0, 1, 0]
next_state, reward, done, _ = env.step(action)
# After training (or loading the model), you should run the evaluation:
while True:
state, done = env.reset(True), False
