def load_envs_and_config(file_path, device):
save_dict = torch.load(file_path)
config = save_dict['config']
config['device'] = device
env_factory = EnvFactory(config=config)
virtual_env = env_factory.generate_virtual_env()
virtual_env.load_state_dict(save_dict['model'])
real_env = env_factory.generate_real_env()
return virtual_env, real_env, config
def load_envs_and_config(dir, model_file_name):
file_path = os.path.join(dir, model_file_name)
save_dict = torch.load(file_path)
config = save_dict['config']
# config['envs']['CartPole-v0']['solved_reward'] = 195
# config['envs']['CartPole-v0']['max_steps'] = 200
env_factory = EnvFactory(config=config)
virtual_env = env_factory.generate_virtual_env()
virtual_env.load_state_dict(save_dict['model'])
real_env = env_factory.generate_real_env()
return virtual_env, real_env, config
def load_envs_and_config(dir, file_name):
file_path = os.path.join(dir, file_name)
save_dict = torch.load(file_path)
config = save_dict['config']
env_factory = EnvFactory(config=config)
virtual_env = env_factory.generate_virtual_env()
virtual_env.load_state_dict(save_dict['model'])
real_env = env_factory.generate_real_env()
grid_size, _, _ = file_name.split('_')
M, N = grid_size.split('x')
return virtual_env, real_env, config, int(M), int(N)
def load_envs_and_config(file_name, model_dir, device):
file_path = os.path.join(model_dir, file_name)
save_dict = torch.load(file_path)
config = save_dict['config']
config['device'] = device
env_factory = EnvFactory(config=config)
virtual_env = env_factory.generate_virtual_env()
virtual_env.load_state_dict(save_dict['model'])
real_env = env_factory.generate_real_env()
# load additional agent configs
with open("../default_config_acrobot.yaml", "r") as stream:
config_new = yaml.safe_load(stream)["agents"]
config["agents"]["duelingddqn"] = config_new["duelingddqn"]
config["agents"]["duelingddqn_vary"] = config_new["duelingddqn_vary"]
return virtual_env, real_env, config
config_mod['agents'][
self.agent_name]['hidden_size'] = config['hidden_size']
config_mod['agents'][
self.agent_name]['hidden_layer'] = config['hidden_layer']
print("full config: ", config_mod['agents'][self.agent_name])
return config_mod
if __name__ == "__main__":
with open("../default_config_cartpole.yaml", "r") as stream:
config = yaml.safe_load(stream)
torch.set_num_threads(1)
# generate environment
env_fac = EnvFactory(config)
virt_env = env_fac.generate_virtual_env()
real_env = env_fac.generate_real_env()
timing = []
for i in range(10):
ddqn = DDQN_vary(env=real_env, config=config, icm=True)
# ddqn.train(env=virt_env, time_remaining=50)
print('TRAIN')
ddqn.train(env=real_env, time_remaining=500)
# print('TEST')
# ddqn.test(env=real_env, time_remaining=500)
print('avg. ' + str(sum(timing) / len(timing)))