def main(_):
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=calc_gpu_fraction(FLAGS.gpu_fraction))
with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
config = get_config(FLAGS) or FLAGS
if config.env_type == 'simple':
env = SimpleGymEnvironment(config)
else:
env = GymEnvironment(config)
ACPconfig = ACPConfig(env)
if not tf.test.is_gpu_available() and FLAGS.use_gpu:
raise Exception("use_gpu flag is true when no GPUs are available")
if not FLAGS.use_gpu:
config.cnn_format = 'NHWC'
# Becuase of code shittines, these steps should be after each other!
acpAgent = acp.acp(sess, ACPconfig)
agentDQN = Agent(config, env, acpAgent, sess)
acpAgent.setdir(agentDQN.model_dir)
sess.run(tf.initializers.global_variables())
# Load both models if exist any checkpoint
acpAgent.load()
agentDQN.load()
if FLAGS.is_train:
agentDQN.train()
else:
raise Exception('agentDQN.play() is Not Implemented')
agentDQN.play()
deque(maxlen=200) for _ in range(4)
]
agent_obs = [None] * flags.num_agents
agent_obs_buffer = [None] * flags.num_agents
agent_action_buffer = [2] * flags.num_agents
max_steps = 8 * (flags.grid_width + flags.grid_height)
start_time = time.time()
# Load an RL agent and initialize it from checkpoint if necessary
if flags.agent_type == "dqn":
agent = DQN_Agent(state_size, action_size, flags.num_agents)
elif flags.agent_type == "ppo":
agent = PPO_Agent(state_size, action_size, flags.num_agents)
if flags.load_model:
start, eps = agent.load(project_root / 'checkpoints', 0, 1.0)
else:
start, eps = 0, 1.0
if not flags.train:
eps = 0.0
# We don't want to retrain on old railway networks when we restart from a checkpoint, so we just loop
# through the generators to get all the old networks out of the way
if start > 0: print(f"Skipping {start} railways")
for _ in range(0, start):
rail_generator()
schedule_generator()
# Helper function to detect collisions
ACTIONS = {0: 'B', 1: 'L', 2: 'F', 3: 'R', 4: 'S'}
