def main():
writer = SummaryWriter()
env = gym.make('MineRLObtainDiamondDense-v0')
if FRAME_SKIP > 0:
env = FrameSkip(env, FRAME_SKIP)
env = ObsWrapper(env)
env = MoveAxisWrapper(env, -1, 0)
env = CombineActionWrapper(env)
agent = Agent(env.observation_space, env.action_space)
data = minerl.data.make('MineRLTreechop-v0', data_dir=MINERL_DATA_ROOT)
data_source = data.sarsd_iter(num_epochs=-1, max_sequence_len=DATA_BATCH_SIZE)
# data_2 = minerl.data.make('MineRLObtainDiamond-v0', data_dir=MINERL_DATA_ROOT)
# data_2_source = data.sarsd_iter(num_epochs=-1, max_sequence_len=128)
# behavioral cloning
train_from_expert(agent, data_source)
net_steps = 0
n_episode = 0
while True:
obs = env.reset()
done = False
netr = 0
net_bonus_r = 0
nobs = None
step = 0
while not done:
action = agent.act(obs)
nobs, reward, done, info = env.step(action)
netr += reward
reward += agent.bonus_reward(obs, action, nobs)
net_bonus_r += reward
agent.add_data(obs, action, reward, nobs, done)
obs = nobs
# To get better view in your training phase, it is suggested
# to register progress continuously, example when 54% completed
# aicrowd_helper.register_progress(0.54)
# To fetch latest information from instance manager, you can run below when you want to know the state
#>> parser.update_information()
#>> print(parser.payload)
# .payload: provide AIcrowd generated json
# Example: {'state': 'RUNNING', 'score': {'score': 0.0, 'score_secondary': 0.0}, 'instances': {'1': {'totalNumberSteps': 2001, 'totalNumberEpisodes': 0, 'currentEnvironment': 'MineRLObtainDiamond-v0', 'state': 'IN_PROGRESS', 'episodes': [{'numTicks': 2001, 'environment': 'MineRLObtainDiamond-v0', 'rewards': 0.0, 'state': 'IN_PROGRESS'}], 'score': {'score': 0.0, 'score_secondary': 0.0}}}}
# .current_state: provide indepth state information avaiable as dictionary (key: instance id)
step += 1
net_steps += 1
if (TRAIN_INTERVAL != 0 and step % TRAIN_INTERVAL == 0) or done:
total_discrim_loss = 0.0
total_value = total_ppo_loss = total_value_loss = total_entropy = 0
n_epoch = 0
while not agent.is_memory_empty():
s, a, _, _, _ = data_source.__next__()
s = data_state_wrapper(s)
a = data_action_wrapper(a)
total_discrim_loss += agent.train_discriminator(s, a)
value, ppo_loss, value_loss, entropy = agent.train_policy()
total_value += value
total_ppo_loss += ppo_loss
total_value_loss += value_loss
total_entropy += entropy
n_epoch += 1
writer.add_scalar('Train/Value', value / n_epoch, net_steps)
writer.add_scalar('Train/PolicyLoss', ppo_loss / n_epoch, net_steps)
writer.add_scalar('Train/ValueLoss', value_loss / n_epoch, net_steps)
writer.add_scalar('Train/Entropy', entropy / n_epoch, net_steps)
writer.add_scalar('Train/DiscriminatorLoss', total_discrim_loss / n_epoch, net_steps)
agent.save_model()
writer.add_scalar('Reward/ExternalReward', netr, n_episode)
writer.add_scalar('Reward/TotalReward', net_bonus_r, n_episode)
n_episode += 1
agent.save_model()
agent.save_model()
aicrowd_helper.register_progress(1)
env.close()
