def main():
# 0.99996 for 30000 iterations
# 0.999 for 1000 iterations
# 0.9998 for 10000 iterations
# 0.99995 for 20000
# 0.999965 for 40000
# 0.999955 for 50000
# 0.999975 for 60000
# 0.999977 for 100000
# 0.999993 for 200000
# 0.999997 for 500000
# 0.999997for 1000000
# 0.999999 for 2000000
# 0.9999994 for 3000000
# 0.9999997 for 6000000
robot = SwimmingRobot(t_interval=8)
trial_name = 'DQN_swimming_w_theta_forward_20000_iters'
trial_num = 0
reward_function = forward_reward_function
episodes = 20
iterations = 1000
total_iterations = episodes * iterations
network_update_freq = 20
batch_size = 8
epsilon_decay = 0.99995
learning_rate = 2e-4
model_architecture = (50, 10)
dqn_agent = DQN_Agent(robot=robot,
reward_function=reward_function,
trial_name=trial_name,
trial_num=trial_num,
episodes=episodes,
iterations=iterations,
network_update_freq=network_update_freq,
check_singularity=False,
input_dim=5,
output_dim=1,
actions_params=(-pi/8, pi/8, pi/8),
model_architecture=model_architecture,
memory_size=total_iterations//50,
memory_buffer_coef=20,
randomize_theta=False,
batch_size=batch_size,
gamma=0.99,
epsilon=1.0,
epsilon_min=0.1,
epsilon_decay=epsilon_decay,
learning_rate=learning_rate,
params=None)
dqn_agent.run()
def main():
# 0.99996 for 30000 iterations
# 0.999 for 1000 iterations
# 0.9998 for 10000 iterations
# 0.99995 for 20000
# 0.999965 for 40000
# 0.99996 for 50000
# 0.999975 for 60000
# 0.999985 for 100000
# 0.999993 for 200000
# 0.999997 for 500000
# 0.9999987 for 1000000
# 0.999999 for 2000000
# 0.9999994 for 3000000
# 0.9999997 for 6000000
robot = ThreeLinkRobot(t_interval=4)
trial_name = 'DQN_wheeled_w_theta_largest_action_50000_iters'
trial_num = 29
episodes = 50
iterations = 1000
total_iterations = episodes * iterations
network_update_freq = 50
batch_size = 8
epsilon_decay = 0.99996
learning_rate = 2e-4
dqn_agent = DQN_Agent(robot=robot,
reward_function=reward_function,
trial_name=trial_name,
trial_num=trial_num,
episodes=episodes,
iterations=iterations,
network_update_freq=network_update_freq,
check_singularity=True,
input_dim=5,
output_dim=1,
actions_params=(-pi / 8, pi / 8, pi / 8),
model_architecture=(50, 10),
memory_size=total_iterations // 50,
memory_buffer_coef=20,
randomize_theta=False,
batch_size=batch_size,
gamma=0.99,
epsilon=1.0,
epsilon_min=0.1,
epsilon_decay=epsilon_decay,
learning_rate=learning_rate)
dqn_agent.run()
def main():
robot_type = args.robot_type
if robot_type == "swimming":
robot = SwimmingRobot(t_interval=args.t_interval,
a_upper=args.a_upper,
a_lower=args.a_lower,
no_joint_limit=args.no_joint_limit)
check_singularity = False
elif robot_type == "wheeled":
robot = ThreeLinkRobot(t_interval=args.t_interval)
check_singularity = True
else:
raise ValueError("Unknown robot type: {}".format(robot_type))
episodes = args.episodes
iterations = args.iterations
total_iterations = episodes * iterations
if args.reward_func == "forward":
reward_function = forward_reward_function
elif args.reward_func == "left":
reward_function = left_reward_function
else:
raise ValueError("Unknown reward function: {}".format(args.reward_func))
network_update_freq = args.network_update_freq
batch_size = args.batch_size
epsilon_min = args.epsilon_min
epsilon_decay = epsilon_min ** (1/total_iterations)
learning_rate = args.learning_rate
model_architecture = [int(num) for num in args.model_architecture.split(' ')]
trial_num = args.trial_num
trial_name = 'DQN_{}_{}_{}_iters'.format(robot_type, args.reward_func, total_iterations)
if args.trial_note:
trial_name += "_{}".format(args.trial_note)
params = {
"robot_type": args.robot_type,
"t_interval": args.t_interval,
"a_upper": args.a_upper,
"a_lower": args.a_lower,
"no_joint_limit:": args.no_joint_limit,
"trial_num": args.trial_num,
"trial_note": args.trial_note,
"episodes": args.episodes,
"iterations": args.iterations,
"reward_func": args.reward_func,
"network_update_freq": args.network_update_freq,
"epsilon_min": args.epsilon_min,
"batch_size": args.batch_size,
"learning_rate": args.learning_rate,
"model_architecture": args.model_architecture,
}
dqn_agent = DQN_Agent(robot=robot,
reward_function=reward_function,
trial_name=trial_name,
trial_num=trial_num,
episodes=episodes,
iterations=iterations,
network_update_freq=network_update_freq,
check_singularity=check_singularity,
input_dim=len(robot.state) + 2,
output_dim=1,
actions_params=(-pi/8, pi/8, pi/8),
model_architecture=model_architecture,
memory_size=total_iterations//50,
memory_buffer_coef=5, #5 don't forget to change back to 20!
randomize_theta=False,
batch_size=batch_size,
gamma=0.99,
epsilon=1.0,
epsilon_min=epsilon_min,
epsilon_decay=epsilon_decay,
learning_rate=learning_rate,
params=params)
dqn_agent.run()
def main():
robot_type = args.robot_type
if robot_type == "physical":
robot = PhysicalRobot(delay=args.delay)
check_singularity = False
is_physical_robot = True
else:
raise ValueError("Unknown robot type: {}".format(robot_type))
episodes = args.episodes
iterations = args.iterations
total_iterations = episodes * iterations
if args.reward_func == "forward":
reward_function = physical_forward_reward_function
else:
raise ValueError("Unknown reward function: {}".format(args.reward_func))
a_lower = args.a_lower
a_upper = args.a_upper
a_interval = args.a_interval
action_params = (a_lower, a_upper, a_interval)
network_update_freq = args.network_update_freq
batch_size = args.batch_size
epsilon_min = args.epsilon_min
epsilon_decay = epsilon_min **(1/total_iterations)
learning_rate = args.learning_rate
model_architecture = [int(num) for num in args.model_architecture.split('_')]
trial_num = args.trial_num
trial_name = 'DQN_{}_{}_{}_iters'.format(robot_type, args.reward_func, total_iterations)
if args.trial_note:
trial_name += "_{}".format(args.trial_note)
params = {
"robot_type": args.robot_type,
"delay": args.delay,
"a_lower": args.a_lower,
"a_upper": args.a_upper,
"a_interval": args.a_interval,
"trial_num": args.trial_num,
"trial_note": args.trial_note,
"episodes": args.episodes,
"iterations": args.iterations,
"reward_func": args.reward_func,
"network_update_freq": args.network_update_freq,
"epsilon_min": args.epsilon_min,
"batch_size": args.batch_size,
"learning_rate": args.learning_rate,
"model_architecture": args.model_architecture,
}
dqn_agent = DQN_Agent(robot=robot,
reward_function=reward_function,
trial_name=trial_name,
trial_num=trial_num,
episodes=episodes,
iterations=iterations,
network_update_freq=network_update_freq,
check_singularity=check_singularity,
is_physical_robot=is_physical_robot,
input_dim=len(robot.state) + 2,
output_dim=1,
actions_params=action_params,
model_architecture=model_architecture,
memory_size=total_iterations//5, #10
memory_buffer_coef=20, #20
randomize_theta=False,
batch_size=batch_size,
gamma=0.99,
epsilon=1.0,
epsilon_min=epsilon_min,
epsilon_decay=epsilon_decay,
learning_rate=learning_rate,
params=params)
dqn_agent.run()