def step(self, obs):
super(MarineAgent, self).step(obs)
#1. Select marine!
obs, screen, player = common.select_marine(self.env, [obs])
player_relative = obs[0].observation["screen"][_PLAYER_RELATIVE]
enemy_y, enemy_x = (player_relative == _PLAYER_HOSTILE).nonzero()
#2. Run away from nearby enemy
closest, min_dist = None, None
print(len(player))
if(len(player) == 2):
for p in zip(enemy_x, enemy_y):
dist = np.linalg.norm(np.array(player) - np.array(p))
if not min_dist or dist < min_dist:
closest, min_dist = p, dist
#3. Sparse!
friendly_y, friendly_x = (player_relative == _PLAYER_FRIENDLY).nonzero()
closest_friend, min_dist_friend = None, None
if(len(player) == 2):
for p in zip(friendly_x, friendly_y):
dist = np.linalg.norm(np.array(player) - np.array(p))
if not min_dist_friend or dist < min_dist_friend:
closest_friend, min_dist_friend = p, dist
if(min_dist != None and min_dist <= 1):
obs, new_action = common.marine_action(self.env, obs, player, 2)
elif(min_dist_friend != None):
sparse_or_attack = 1
obs, new_action = common.marine_action(self.env, obs, player, sparse_or_attack)
else:
obs, new_action = common.marine_action(self.env, obs, player, 1)
return new_action[0]
def step(self, obs):
super(MarineAgent, self).step(obs)
#1. Select marine!
obs, screen, player = common.select_marine(self.env, [obs])
player_relative = obs[0].observation["screen"][_PLAYER_RELATIVE]
enemy_y, enemy_x = (player_relative == _PLAYER_HOSTILE).nonzero()
#2. Run away from nearby enemy
closest, min_dist = None, None
if (len(player) == 2):
for p in zip(enemy_x, enemy_y):
dist = np.linalg.norm(np.array(player) - np.array(p))
if not min_dist or dist < min_dist:
closest, min_dist = p, dist
#3. Sparse!
friendly_y, friendly_x = (
player_relative == _PLAYER_FRIENDLY).nonzero()
closest_friend, min_dist_friend = None, None
if (len(player) == 2):
for p in zip(friendly_x, friendly_y):
dist = np.linalg.norm(np.array(player) - np.array(p))
if not min_dist_friend or dist < min_dist_friend:
closest_friend, min_dist_friend = p, dist
if (min_dist != None and min_dist <= 7):
obs, new_action = common.marine_action(self.env, obs, player, 2)
elif (min_dist_friend != None and min_dist_friend <= 3):
sparse_or_attack = np.random.randint(0, 2)
obs, new_action = common.marine_action(self.env, obs, player,
sparse_or_attack)
else:
obs, new_action = common.marine_action(self.env, obs, player, 1)
return new_action[0]
def learn(env,
q_func,
num_actions=3,
lr=5e-4,
max_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_final_eps=0.02,
train_freq=1,
batch_size=32,
print_freq=1,
checkpoint_freq=10000,
learning_starts=1000,
gamma=1.0,
target_network_update_freq=500,
prioritized_replay=False,
prioritized_replay_alpha=0.6,
prioritized_replay_beta0=0.4,
prioritized_replay_beta_iters=None,
prioritized_replay_eps=1e-6,
num_cpu=16,
param_noise=False,
param_noise_threshold=0.05,
callback=None,
demo_replay=[]
):
"""Train a deepq model.
Parameters
-------
env: pysc2.env.SC2Env
environment to train on
q_func: (tf.Variable, int, str, bool) -> tf.Variable
the model that takes the following inputs:
observation_in: object
the output of observation placeholder
num_actions: int
number of actions
scope: str
reuse: bool
should be passed to outer variable scope
and returns a tensor of shape (batch_size, num_actions) with values of every action.
lr: float
learning rate for adam optimizer
max_timesteps: int
number of env steps to optimizer for
buffer_size: int
size of the replay buffer
exploration_fraction: float
fraction of entire training period over which the exploration rate is annealed
exploration_final_eps: float
final value of random action probability
train_freq: int
update the model every `train_freq` steps.
set to None to disable printing
batch_size: int
size of a batched sampled from replay buffer for training
print_freq: int
how often to print out training progress
set to None to disable printing
checkpoint_freq: int
how often to save the model. This is so that the best version is restored
at the end of the training. If you do not wish to restore the best version at
the end of the training set this variable to None.
learning_starts: int
how many steps of the model to collect transitions for before learning starts
gamma: float
discount factor
target_network_update_freq: int
update the target network every `target_network_update_freq` steps.
prioritized_replay: True
if True prioritized replay buffer will be used.
prioritized_replay_alpha: float
alpha parameter for prioritized replay buffer
prioritized_replay_beta0: float
initial value of beta for prioritized replay buffer
prioritized_replay_beta_iters: int
number of iterations over which beta will be annealed from initial value
to 1.0. If set to None equals to max_timesteps.
prioritized_replay_eps: float
epsilon to add to the TD errors when updating priorities.
num_cpu: int
number of cpus to use for training
callback: (locals, globals) -> None
function called at every steps with state of the algorithm.
If callback returns true training stops.
Returns
-------
act: ActWrapper
Wrapper over act function. Adds ability to save it and load it.
See header of baselines/deepq/categorical.py for details on the act function.
"""
# Create all the functions necessary to train the model
sess = U.make_session(num_cpu=num_cpu)
sess.__enter__()
def make_obs_ph(name):
return U.BatchInput((64, 64), name=name)
act, train, update_target, debug = deepq.build_train(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=num_actions,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=10
)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': num_actions,
}
# Create the replay buffer
if prioritized_replay:
replay_buffer = PrioritizedReplayBuffer(buffer_size, alpha=prioritized_replay_alpha)
if prioritized_replay_beta_iters is None:
prioritized_replay_beta_iters = max_timesteps
beta_schedule = LinearSchedule(prioritized_replay_beta_iters,
initial_p=prioritized_replay_beta0,
final_p=1.0)
else:
replay_buffer = ReplayBuffer(buffer_size)
beta_schedule = None
# Create the schedule for exploration starting from 1.
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction * max_timesteps),
initial_p=1.0,
final_p=exploration_final_eps)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
episode_rewards = [0.0]
saved_mean_reward = None
obs = env.reset()
# Select all marines first
player_relative = obs[0].observation["screen"][_PLAYER_RELATIVE]
screen = player_relative
obs = common.init(env, obs)
group_id = 0
reset = True
with tempfile.TemporaryDirectory() as td:
model_saved = False
model_file = os.path.join(td, "model")
for t in range(max_timesteps):
if callback is not None:
if callback(locals(), globals()):
break
# Take action and update exploration to the newest value
kwargs = {}
if not param_noise:
update_eps = exploration.value(t)
update_param_noise_threshold = 0.
else:
update_eps = 0.
if param_noise_threshold >= 0.:
update_param_noise_threshold = param_noise_threshold
else:
# Compute the threshold such that the KL divergence between perturbed and non-perturbed
# policy is comparable to eps-greedy exploration with eps = exploration.value(t).
# See Appendix C.1 in Parameter Space Noise for Exploration, Plappert et al., 2017
# for detailed explanation.
update_param_noise_threshold = -np.log(1. - exploration.value(t) + exploration.value(t) / float(num_actions))
kwargs['reset'] = reset
kwargs['update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
# custom process for DefeatZerglingsAndBanelings
obs, screen, player = common.select_marine(env, obs)
action = act(np.array(screen)[None], update_eps=update_eps, **kwargs)[0]
reset = False
rew = 0
new_action = None
obs, new_action = common.marine_action(env, obs, player, action)
army_count = env._obs.observation.player_common.army_count
try:
if army_count > 0 and _ATTACK_SCREEN in obs[0].observation["available_actions"]:
obs = env.step(actions=new_action)
else:
new_action = [sc2_actions.FunctionCall(_NO_OP, [])]
obs = env.step(actions=new_action)
except Exception as e:
#print(e)
1 # Do nothing
player_relative = obs[0].observation["screen"][_PLAYER_RELATIVE]
new_screen = player_relative
rew += obs[0].reward
done = obs[0].step_type == environment.StepType.LAST
selected = obs[0].observation["screen"][_SELECTED]
player_y, player_x = (selected == _PLAYER_FRIENDLY).nonzero()
if(len(player_y)>0):
player = [int(player_x.mean()), int(player_y.mean())]
if(len(player) == 2):
if(player[0]>32):
new_screen = common.shift(LEFT, player[0]-32, new_screen)
elif(player[0]<32):
new_screen = common.shift(RIGHT, 32 - player[0], new_screen)
if(player[1]>32):
new_screen = common.shift(UP, player[1]-32, new_screen)
elif(player[1]<32):
new_screen = common.shift(DOWN, 32 - player[1], new_screen)
# Store transition in the replay buffer.
replay_buffer.add(screen, action, rew, new_screen, float(done))
screen = new_screen
episode_rewards[-1] += rew
reward = episode_rewards[-1]
if done:
print("Episode Reward : %s" % episode_rewards[-1])
obs = env.reset()
player_relative = obs[0].observation["screen"][_PLAYER_RELATIVE]
screen = player_relative
group_list = common.init(env, obs)
# Select all marines first
#env.step(actions=[sc2_actions.FunctionCall(_SELECT_UNIT, [_SELECT_ALL])])
episode_rewards.append(0.0)
reset = True
if t > learning_starts and t % train_freq == 0:
# Minimize the error in Bellman's equation on a batch sampled from replay buffer.
if prioritized_replay:
experience = replay_buffer.sample(batch_size, beta=beta_schedule.value(t))
(obses_t, actions, rewards, obses_tp1, dones, weights, batch_idxes) = experience
else:
obses_t, actions, rewards, obses_tp1, dones = replay_buffer.sample(batch_size)
weights, batch_idxes = np.ones_like(rewards), None
td_errors = train(obses_t, actions, rewards, obses_tp1, dones, weights)
if prioritized_replay:
new_priorities = np.abs(td_errors) + prioritized_replay_eps
replay_buffer.update_priorities(batch_idxes, new_priorities)
if t > learning_starts and t % target_network_update_freq == 0:
# Update target network periodically.
update_target()
mean_100ep_reward = round(np.mean(episode_rewards[-101:-1]), 1)
num_episodes = len(episode_rewards)
if done and print_freq is not None and len(episode_rewards) % print_freq == 0:
logger.record_tabular("steps", t)
logger.record_tabular("episodes", num_episodes)
logger.record_tabular("reward", reward)
logger.record_tabular("mean 100 episode reward", mean_100ep_reward)
logger.record_tabular("% time spent exploring", int(100 * exploration.value(t)))
logger.dump_tabular()
if (checkpoint_freq is not None and t > learning_starts and
num_episodes > 100 and t % checkpoint_freq == 0):
if saved_mean_reward is None or mean_100ep_reward > saved_mean_reward:
if print_freq is not None:
logger.log("Saving model due to mean reward increase: {} -> {}".format(
saved_mean_reward, mean_100ep_reward))
U.save_state(model_file)
model_saved = True
saved_mean_reward = mean_100ep_reward
if model_saved:
if print_freq is not None:
logger.log("Restored model with mean reward: {}".format(saved_mean_reward))
U.load_state(model_file)
return ActWrapper(act)