def run(halite_env: BaseEnv, load_latest: bool=False):
config = halite_env.config
model_path, run_num, run_dir, log_dir = run_setup(config.model_name, get_latest_model=load_latest)
os.makedirs(log_dir)
logger = SummaryWriter(str(log_dir))
torch.manual_seed(run_num)
np.random.seed(run_num)
# Build MAAC model
if model_path is None:
model = AttentionSAC(halite_env.agent_type_topologies,
tau=config.tau,
pi_lr=config.pi_lr,
q_lr=config.q_lr,
gamma=config.gamma,
pol_hidden_dim=config.pol_hidden_dim,
critic_hidden_dim=config.critic_hidden_dim,
attend_heads=config.attend_heads,
reward_scale=config.reward_scale)
else:
model = AttentionSAC.init_from_save(model_path, load_critic=True)
# Build replay buffer
replay_buffer = ReplayBuffer(config.buffer_length)
prev_time = time.perf_counter()
t = 0
for ep_i in range(0, config.n_episodes, config.n_rollout_threads):
curr_time = time.perf_counter()
print("Episodes %i-%i of %i (%is)" % (ep_i + 1,
ep_i + 1 + config.n_rollout_threads,
config.n_episodes,
(curr_time - prev_time)))
model.prep_rollouts(device='cpu')
game_reward = halite_env.simulate(lambda o: model.step(o, explore=True), replay_buffer)
t += config.n_rollout_threads
if (replay_buffer.length() >= config.batch_size and
(t % config.games_per_update) < config.n_rollout_threads):
print("Training")
if config.use_gpu:
model.prep_training(device='gpu')
else:
model.prep_training(device='cpu')
for u_i in range(config.num_updates):
sample: List[Dict[AgentKey, AgentReplayFrame]] = replay_buffer.sample(config.batch_size)
# print("Original sample size", len(sample))
# print("Preprocessing to batch structure")
sample: Dict[AgentKey, BatchedAgentReplayFrame] = preprocess_to_batch(sample, to_gpu=config.use_gpu)
# print("Filtered sample size", len(sample))
# if len(sample) < 5:
# print("Sample size keys:", sample.keys())
# print("Updating model critic")
model.update_critic(sample, logger=logger)
# print("Updating model policies")
model.update_policies(sample, logger=logger)
model.update_all_targets()
model.prep_rollouts(device='cpu')
ep_rews = replay_buffer.get_average_rewards(config.episode_length * config.n_rollout_threads)
for k, v in ep_rews.items():
logger.add_scalar('agent%s/mean_episode_rewards' % str(k), v, ep_i)
logger.add_scalar("global_env_rewards", game_reward, ep_i)
if ep_i % config.save_interval < config.n_rollout_threads:
print("Saving")
model.prep_rollouts(device='cpu')
os.makedirs(run_dir / 'incremental', exist_ok=True)
model.save(run_dir / 'incremental' / ('model_ep%i.pt' % (ep_i + 1)))
model.save(run_dir / 'model.pt')
print("run_dir", run_dir)
prev_time = curr_time
model.save(run_dir / 'model.pt')
logger.export_scalars_to_json(str(log_dir / 'summary.json'))
logger.close()
def train(env):
n_agents = env["n_agents"]
x_dim = env["x_dim"]
y_dim = env["y_dim"]
n_cities = env["n_cities"]
max_rails_between_cities = env["max_rails_between_cities"]
max_rails_in_city = env["max_rails_in_city"]
seed = 0
use_fast_tree_obs = False
# Observation parameters
observation_tree_depth = 4
observation_radius = 10
observation_max_path_depth = 30
# Set the seeds
random.seed(seed)
np.random.seed(seed)
# Break agents from time to time
malfunction_parameters = MalfunctionParameters(
malfunction_rate=1. / 10000, # Rate of malfunctions
min_duration=15, # Minimal duration
max_duration=50 # Max duration
)
# Observation builder
predictor = ShortestPathPredictorForRailEnv(observation_max_path_depth)
tree_observation = None
if use_fast_tree_obs:
tree_observation = FastTreeObs(max_depth=observation_tree_depth)
print("Using FastTreeObs")
else:
tree_observation = TreeObsForRailEnv(max_depth=observation_tree_depth,
predictor=predictor)
print("Using StandardTreeObs")
speed_profiles = {
1.: 1.0, # Fast passenger train
1. / 2.: 0.0, # Fast freight train
1. / 3.: 0.0, # Slow commuter train
1. / 4.: 0.0 # Slow freight train
}
env = RailEnv(
width=x_dim,
height=y_dim,
rail_generator=sparse_rail_generator(
max_num_cities=n_cities,
grid_mode=False,
max_rails_between_cities=max_rails_between_cities,
max_rails_in_city=max_rails_in_city),
schedule_generator=sparse_schedule_generator(speed_profiles),
number_of_agents=n_agents,
malfunction_generator_and_process_data=malfunction_from_params(
malfunction_parameters),
obs_builder_object=tree_observation,
random_seed=seed)
rewards = []
obs, info = env.reset()
if use_fast_tree_obs:
state_size = tree_observation.observation_dim
else:
# Calculate the state size given the depth of the tree observation and the
# number of features
n_features_per_node = env.obs_builder.observation_dim
n_nodes = 0
for i in range(observation_tree_depth + 1):
n_nodes += np.power(4, i)
state_size = n_features_per_node * n_nodes
action_size = 5
DEVICE = 'cpu'
# if torch.cuda.is_available():
# DEVICE = 'gpu'
buffer_length = 10000
steps_to_save_model = 10
step_size = 100
num_steps = 100 # update every 100 steps
avg_steps = 20 # num steps to average and plot rewards
reward_q = []
batch_size = 100
agent_obs = np.array([None] * env.get_num_agents())
max_steps = int(4 * 2 * (env.height + env.width + (n_agents / n_cities)))
num_episodes = 100000
agent_init_params = []
sa_size = []
for i in range(n_agents):
agent_init_params.append({
'num_in_pol': state_size,
'num_out_pol': action_size,
'init_weights': 'model.pt'
})
sa_size.append((state_size, action_size))
hyperparams = {
"tau": 0.01,
"pi_lr": 0.00001,
"q_lr": 0.00005,
"pol_hidden_dim": 256,
"critic_hidden_dim": 256,
"attend_heads": 8
}
model = AttentionSAC(agent_init_params=agent_init_params,
sa_size=sa_size,
tau=hyperparams["tau"],
pi_lr=hyperparams["pi_lr"],
q_lr=hyperparams["q_lr"],
pol_hidden_dim=hyperparams["pol_hidden_dim"],
critic_hidden_dim=hyperparams["critic_hidden_dim"],
attend_heads=hyperparams["attend_heads"])
model.init_dict = {}
replay_buffer = ReplayBuffer(buffer_length, n_agents,
[state_size for i in range(n_agents)],
[action_size for i in range(n_agents)])
print("MAX STEPS: " + str(max_steps))
print("NUM EPISODES: ", num_episodes)
print("HYPERPARAMS: ")
print(hyperparams)
start_time = time.time()
for ep in range(num_episodes):
print("Episode " + str(ep) + ":", flush=True)
obs, info = env.reset(True, True)
model.prep_rollouts(device=DEVICE)
reward_sum_for_this_episode = 0
for steps in range(max_steps):
if steps % step_size == 0:
print("=", end="", flush=True)
for agent in env.get_agent_handles():
if obs[agent] is not None:
if use_fast_tree_obs:
agent_obs[agent] = obs[agent]
else:
agent_obs[agent] = normalize_observation(
obs[agent],
observation_tree_depth,
observation_radius=observation_radius)
else:
agent_obs[agent] = np.array([0.] * state_size)
action_dict = {}
agent_actions = []
torch_obs = [
Variable(torch.Tensor([agent_obs[i]]), requires_grad=False)
for i in range(n_agents)
]
torch_agent_actions = model.step(torch_obs, explore=True)
agent_actions = [ac.data.numpy() for ac in torch_agent_actions]
for i in range(n_agents):
dist = torch_agent_actions[i][0]
idx = -1
for j in range(action_size):
if dist[j] != 0:
idx = j
break
action_dict[i] = idx
next_obs, all_rewards, done, info = env.step(action_dict)
rewards = []
dones = []
next_agent_obs = np.array([None] * env.get_num_agents())
for agent in env.get_agent_handles():
if next_obs[agent] is not None:
if use_fast_tree_obs:
next_agent_obs[agent] = next_obs[agent]
else:
next_agent_obs[agent] = normalize_observation(
obs[agent],
observation_tree_depth,
observation_radius=observation_radius)
else:
next_agent_obs[agent] = np.array([0.] * state_size)
for i in range(n_agents):
reward_sum_for_this_episode += all_rewards[i]
rewards.append(all_rewards[i])
all_rewards[i] += augment_reward(agent_obs[agent])
dones.append(done[i])
replay_buffer.push(np.array([agent_obs]), np.array(agent_actions),
np.array([rewards]), np.array([next_agent_obs]),
np.array([dones]))
if steps % num_steps == 0:
model.prep_training(device=DEVICE)
sample = replay_buffer.sample(batch_size, norm_rews=False)
#print(sample)
model.update_critic(sample)
model.update_policies(sample)
model.update_all_targets()
model.prep_rollouts(device=DEVICE)
reward_sum_for_this_episode /= n_agents
reward_q.append(reward_sum_for_this_episode)
if len(reward_q) == avg_steps:
wandb.log({'reward': np.mean(reward_q)})
reward_q = []
print()
if ep % steps_to_save_model == 0:
print("\nSaving model")
model.save(os.getcwd() + "/model.pt")
cur_time = time.time()
time_elapsed = (cur_time - start_time) // 60
print("Time Elapsed: " + str(time_elapsed) + "\n")
dones.append(done[i])
replay_buffer.push(np.array([agent_obs]), np.array(agent_actions),
np.array([rewards]), np.array([next_agent_obs]),
np.array([dones]))
if steps % num_steps == 0:
model.prep_training(device=DEVICE)
sample = replay_buffer.sample(batch_size, norm_rews=False)
#print(sample)
model.update_critic(sample)
model.update_policies(sample)
model.update_all_targets()
model.prep_rollouts(device=DEVICE)
reward_sum_for_this_episode /= n_agents
reward_q.append(reward_sum_for_this_episode)
if len(reward_q) == avg_steps:
wandb.log({'reward': np.mean(reward_q)})
reward_q = []
print()
if ep % steps_to_save_model == 0:
print("\nSaving model")
model.save(os.getcwd() + "/model.pt")
cur_time = time.time()
time_elapsed = (cur_time - start_time) // 60
print("Time Elapsed: " + str(time_elapsed) + "\n")