def populate_replay_buffer(replay_buffer, action_sampler, env):
print("Populating replay memory...")
state = env.reset()
state = StatePreprocessor.process(state)
done = False
for t in itertools.count():
if done:
break
action_probs = action_sampler(state)
action = np.random.choice(np.arange(len(action_probs)), p=action_probs)
print("Step {step} state: {state}, action: {action}.".format(
step=t, state=state, action=action))
next_state, reward, done = env.execute(action=action)
next_state = StatePreprocessor.process(next_state)
replay_buffer.push(state, action, next_state, done, reward)
state = next_state
def deep_q_learning(sess,
env,
q_estimator,
target_estimator,
num_steps,
experiment_dir,
replay_memory_size=5000,
update_target_estimator_every=500,
discount_factor=0.999,
epsilon_start=1.0,
epsilon_end=0.1,
epsilon_decay_steps=10000,
update_q_values_every=4,
batch_size=32,
restore=True):
# Create directories for checkpoints and summaries
checkpoint_dir = os.path.join(experiment_dir, "checkpoints")
if not os.path.exists(checkpoint_dir):
os.makedirs(checkpoint_dir)
checkpoint_path = os.path.join(checkpoint_dir, "model")
reward_dir = os.path.join(experiment_dir, "rewards")
if not os.path.exists(reward_dir):
os.makedirs(reward_dir)
reward_writer = tf.summary.FileWriter(reward_dir)
starting_episode = 0
saver = tf.train.Saver()
if restore:
starting_episode = persistence.get_last_episode(reward_dir)
# Load a previous checkpoint if we find one
latest_checkpoint = tf.train.latest_checkpoint(checkpoint_dir)
if latest_checkpoint:
print("Loading model checkpoint {}...\n".format(latest_checkpoint))
saver.restore(sess, latest_checkpoint)
total_t = sess.run(tf.train.get_global_step())
# The epsilon decay schedule
epsilons = np.linspace(epsilon_start, epsilon_end, epsilon_decay_steps)
replay_buffer = PrioritizedReplayBuffer(replay_memory_size,
alpha=0.6,
beta0=0.4,
save_dir=experiment_dir)
reward_shaper = ReplayRewardShaper('../replays/')
reward_shaper.load()
# The policy we're following
policy = make_epsilon_greedy_policy(q_estimator, reward_shaper,
ACTION_SPACE)
# Populate the replay memory with initial experience
action_sampler = lambda state: policy(
sess, state, epsilons[min(total_t, epsilon_decay_steps - 1)])
populate_replay_buffer(replay_buffer, action_sampler, env)
print('Training is starting...')
# Training the agent
for i_episode in itertools.count(starting_episode):
episode_reward = 0
multiplier = 1
# Save the current checkpoint
saver.save(tf.get_default_session(), checkpoint_path)
# Reset the environment
state = env.reset()
state = StatePreprocessor.process(state)
done = False
# One step in the environment
for t in itertools.count():
if total_t >= num_steps:
return
eps = epsilons[min(total_t, epsilon_decay_steps - 1)]
if done or len(state) != STATE_SPACE:
print("Finished episode with reward", episode_reward)
summary = tf.Summary(value=[
tf.Summary.Value(tag="rewards",
simple_value=episode_reward)
])
reward_writer.add_summary(summary, i_episode)
summary = tf.Summary(
value=[tf.Summary.Value(tag="eps", simple_value=eps)])
reward_writer.add_summary(summary, i_episode)
break
# Maybe update the target estimator
if total_t % update_target_estimator_every == 0:
copy_model_parameters(sess, q_estimator, target_estimator)
print("\nCopied model parameters to target network.")
# Take a step
action_probs = policy(sess, state, eps)
action = np.random.choice(np.arange(len(action_probs)),
p=action_probs)
next_state, reward, done = env.execute(action=action)
next_state = StatePreprocessor.process(next_state)
episode_reward += reward * multiplier
multiplier *= discount_factor
# Save transition to replay memory
replay_buffer.push(state, action, next_state, done, reward)
if total_t % update_q_values_every == 0:
# Sample a minibatch from the replay memory
samples, idx = replay_buffer.sample(batch_size)
states, actions, next_states, dones, rewards, _ = map(
np.array, zip(*samples))
# Calculate q values and targets (Double DQN)
next_q_values = q_estimator.predict(sess, next_states)
for i in range(batch_size):
for action in range(ACTION_SPACE):
next_q_values[i][
action] += reward_shaper.get_potential(
next_states[i], action)
next_actions = np.argmax(next_q_values, axis=1)
next_q_values_target = target_estimator.predict(
sess, next_states)
not_dones = np.invert(dones).astype(np.float32)
targets = (
rewards + discount_factor *
reward_shaper.get_potentials(next_states, next_actions) -
reward_shaper.get_potentials(states, actions) +
discount_factor * not_dones *
next_q_values_target[np.arange(batch_size), next_actions])
# Perform gradient descent update
predictions = q_estimator.update(sess, states, actions,
targets)
# Update transition priorities
priors = np.abs(predictions - targets) + EPS_PRIORITY
replay_buffer.update_priorities(idx, priors)
print("\rStep {}, episode {} ({}/{})".format(
t, i_episode, total_t, num_steps),
end="\t")
sys.stdout.flush()
state = next_state
total_t += 1
def do_agent_exploration(updates_queue: multiprocessing.Queue,
q_func_vars_trained_queue: multiprocessing.Queue,
network, seed, config, lr, total_timesteps,
learning_starts, buffer_size, exploration_fraction,
exploration_initial_eps, exploration_final_eps,
train_freq, batch_size, print_freq, checkpoint_freq,
gamma, target_network_update_freq, prioritized_replay,
prioritized_replay_alpha, prioritized_replay_beta0,
prioritized_replay_beta_iters, prioritized_replay_eps,
experiment_name, load_path, network_kwargs):
env = DotaEnvironment()
sess = get_session()
set_global_seeds(seed)
q_func = build_q_func(network, **network_kwargs)
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
act, _, _, debug = deepq.build_train(
scope='deepq_act',
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=env.action_space.n,
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': env.action_space.n,
}
act = ActWrapper(act, act_params)
exploration = LinearSchedule(schedule_timesteps=int(exploration_fraction *
total_timesteps),
initial_p=exploration_initial_eps,
final_p=exploration_final_eps)
U.initialize()
reward_shaper = ActionAdviceRewardShaper(config=config)
reward_shaper.load()
reward_shaper.generate_merged_demo()
full_exp_name = '{}-{}'.format(date.today().strftime('%Y%m%d'),
experiment_name)
experiment_dir = os.path.join('experiments', full_exp_name)
os.makedirs(experiment_dir, exist_ok=True)
summary_dir = os.path.join(experiment_dir, 'summaries')
os.makedirs(summary_dir, exist_ok=True)
summary_writer = tf.summary.FileWriter(summary_dir)
checkpoint_dir = os.path.join(experiment_dir, 'checkpoints')
os.makedirs(checkpoint_dir, exist_ok=True)
stats_dir = os.path.join(experiment_dir, 'stats')
os.makedirs(stats_dir, exist_ok=True)
with tempfile.TemporaryDirectory() as td:
td = checkpoint_dir or td
os.makedirs(td, exist_ok=True)
model_file = os.path.join(td, "best_model")
model_saved = False
saved_mean_reward = None
# if os.path.exists(model_file):
# print('Model is loading')
# load_variables(model_file)
# logger.log('Loaded model from {}'.format(model_file))
# model_saved = True
# elif load_path is not None:
# load_variables(load_path)
# logger.log('Loaded model from {}'.format(load_path))
def synchronize_q_func_vars():
updates_queue.put(
UpdateMessage(UPDATE_STATUS_SEND_WEIGHTS, None, None))
q_func_vars_trained = q_func_vars_trained_queue.get()
update_q_func_expr = []
for var, var_trained in zip(debug['q_func_vars'],
q_func_vars_trained):
update_q_func_expr.append(var.assign(var_trained))
update_q_func_expr = tf.group(*update_q_func_expr)
sess.run(update_q_func_expr)
synchronize_q_func_vars()
episode_rewards = []
act_step_t = 0
while act_step_t < total_timesteps:
# Reset the environment
obs = env.reset()
obs = StatePreprocessor.process(obs)
episode_rewards.append(0.0)
done = False
# Demo preservation variables
demo_picked = 0
demo_picked_step = 0
# Demo switching statistics
demo_switching_stats = [(0, 0)]
# Sample the episode until it is completed
act_started_step_t = act_step_t
while not done:
# Take action and update exploration to the newest value
biases, demo_indexes = reward_shaper.get_action_potentials_with_indexes(
obs, act_step_t)
update_eps = exploration.value(act_step_t)
actions, is_randoms = act(np.array(obs)[None],
biases,
update_eps=update_eps)
action, is_random = actions[0], is_randoms[0]
if not is_random:
bias_demo = demo_indexes[action]
if bias_demo != demo_switching_stats[-1][1]:
demo_switching_stats.append(
(act_step_t - act_started_step_t, bias_demo))
if bias_demo != 0 and demo_picked == 0:
demo_picked = bias_demo
demo_picked_step = act_step_t + 1
pairs = env.step(action)
action, (new_obs, rew, done, _) = pairs[-1]
logger.log(
f'{act_step_t}/{total_timesteps} obs {obs} action {action}'
)
# Compute state on the real reward but learn from the normalized version
episode_rewards[-1] += rew
rew = np.sign(rew) * np.log(1 + np.abs(rew))
new_obs = StatePreprocessor.process(new_obs)
if len(new_obs) == 0:
done = True
else:
transition = (obs, action, rew, new_obs, float(done),
act_step_t)
obs = new_obs
act_step_t += 1
if act_step_t - demo_picked_step >= MIN_STEPS_TO_FOLLOW_DEMO_FOR:
demo_picked = 0
reward_shaper.set_demo_picked(act_step_t, demo_picked)
updates_queue.put(
UpdateMessage(UPDATE_STATUS_CONTINUE, transition,
demo_picked))
# Post episode logging
summary = tf.Summary(value=[
tf.Summary.Value(tag="rewards",
simple_value=episode_rewards[-1])
])
summary_writer.add_summary(summary, act_step_t)
summary = tf.Summary(
value=[tf.Summary.Value(tag="eps", simple_value=update_eps)])
summary_writer.add_summary(summary, act_step_t)
summary = tf.Summary(value=[
tf.Summary.Value(tag="episode_steps",
simple_value=act_step_t - act_started_step_t)
])
summary_writer.add_summary(summary, act_step_t)
mean_5ep_reward = round(float(np.mean(episode_rewards[-5:])), 1)
num_episodes = len(episode_rewards)
if print_freq is not None and num_episodes % print_freq == 0:
logger.record_tabular("steps", act_step_t)
logger.record_tabular("episodes", num_episodes)
logger.record_tabular("mean 5 episode reward", mean_5ep_reward)
logger.record_tabular("% time spent exploring",
int(100 * exploration.value(act_step_t)))
logger.dump_tabular()
# Wait for the learning to finish and synchronize
synchronize_q_func_vars()
# Record demo_switching_stats
if num_episodes % 10 == 0:
save_demo_switching_stats(demo_switching_stats, stats_dir,
num_episodes)
if checkpoint_freq is not None and num_episodes % checkpoint_freq == 0:
# Periodically save the model
rec_model_file = os.path.join(
td, "model_{}_{:.2f}".format(num_episodes,
mean_5ep_reward))
save_variables(rec_model_file)
# Check whether the model is the best so far
if saved_mean_reward is None or mean_5ep_reward > saved_mean_reward:
if print_freq is not None:
logger.log(
"Saving model due to mean reward increase: {} -> {}"
.format(saved_mean_reward, mean_5ep_reward))
save_variables(model_file)
model_saved = True
saved_mean_reward = mean_5ep_reward
updates_queue.put(UpdateMessage(UPDATE_STATUS_FINISH, None, None))
def learn(env,
network,
seed=None,
pool=None,
lr=5e-4,
total_timesteps=100000,
buffer_size=50000,
exploration_fraction=0.1,
exploration_initial_eps=1.0,
exploration_final_eps=0.02,
train_freq=1,
batch_size=32,
print_freq=1,
checkpoint_freq=100,
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,
param_noise=False,
callback=None,
experiment_name='unnamed',
load_path=None,
**network_kwargs):
"""Train a deepq model.
Parameters
-------
env: gym.Env
environment to train on
network: string or a function
neural network to use as a q function approximator. If string, has to be one of the names of registered models in baselines.common.models
(mlp, cnn, conv_only). If a function, should take an observation tensor and return a latent variable tensor, which
will be mapped to the Q function heads (see build_q_func in baselines.deepq.models for details on that)
seed: int or None
prng seed. The runs with the same seed "should" give the same results. If None, no seeding is used.
lr: float
learning rate for adam optimizer
total_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 total_timesteps.
prioritized_replay_eps: float
epsilon to add to the TD errors when updating priorities.
param_noise: bool
whether or not to use parameter space noise (https://arxiv.org/abs/1706.01905)
callback: (locals, globals) -> None
function called at every steps with state of the algorithm.
If callback returns true training stops.
experiment_name: str
name of the experiment (default: trial)
load_path: str
path to load the model from. (default: None)
**network_kwargs
additional keyword arguments to pass to the network builder.
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 = get_session()
set_global_seeds(seed)
q_func = build_q_func(network, **network_kwargs)
# capture the shape outside the closure so that the env object is not serialized
# by cloudpickle when serializing make_obs_ph
observation_space = env.observation_space
def make_obs_ph(name):
return ObservationInput(observation_space, name=name)
act, train, update_target, debug = deepq.build_train(
make_obs_ph=make_obs_ph,
q_func=q_func,
num_actions=env.action_space.n,
optimizer=tf.train.AdamOptimizer(learning_rate=lr),
gamma=gamma,
grad_norm_clipping=10,
param_noise=param_noise)
act_params = {
'make_obs_ph': make_obs_ph,
'q_func': q_func,
'num_actions': env.action_space.n,
}
act = ActWrapper(act, act_params)
# 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 = total_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 *
total_timesteps),
initial_p=exploration_initial_eps,
final_p=exploration_final_eps)
# Initialize the parameters and copy them to the target network.
U.initialize()
update_target()
reward_shaper = ActionAdviceRewardShaper('../completed-observations')
reward_shaper.load()
full_exp_name = '{}-{}'.format(date.today().isoformat(), experiment_name)
experiment_dir = os.path.join('experiments', full_exp_name)
if not os.path.exists(experiment_dir):
os.makedirs(experiment_dir)
summary_dir = os.path.join(experiment_dir, 'summaries')
os.makedirs(summary_dir, exist_ok=True)
summary_writer = tf.summary.FileWriter(summary_dir)
checkpoint_dir = os.path.join(experiment_dir, 'checkpoints')
os.makedirs(checkpoint_dir, exist_ok=True)
with tempfile.TemporaryDirectory() as td:
td = checkpoint_dir or td
os.makedirs(td, exist_ok=True)
model_file = os.path.join(td, "best_model")
model_saved = False
saved_mean_reward = None
if os.path.exists(model_file):
print('Model is loading')
load_variables(model_file)
logger.log('Loaded model from {}'.format(model_file))
model_saved = True
elif load_path is not None:
load_variables(load_path)
logger.log('Loaded model from {}'.format(load_path))
episode_rewards = []
update_step_t = 0
while update_step_t < total_timesteps:
# Reset the environment
obs = env.reset()
obs = StatePreprocessor.process(obs)
episode_rewards.append(0.0)
reset = True
done = False
# Sample the episode until it is completed
act_step_t = update_step_t
while not done:
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(act_step_t)
update_param_noise_threshold = 0.
else:
update_eps = 0.
# Compute the threshold such that the KL divergence between perturbed and non-perturbed
# policy is comparable to eps-greedy exploration with eps = exploration.value(act_step_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(act_step_t) +
exploration.value(act_step_t) /
float(env.action_space.n))
kwargs['reset'] = reset
kwargs[
'update_param_noise_threshold'] = update_param_noise_threshold
kwargs['update_param_noise_scale'] = True
biases = reward_shaper.get_action_potentials(obs)
action = act(np.array(obs)[None],
biases,
update_eps=update_eps,
**kwargs)[0]
reset = False
pairs = env.step(action)
action, (new_obs, rew, done, _) = pairs[-1]
# Write down the real reward but learn from normalized version
episode_rewards[-1] += rew
rew = np.sign(rew) * np.log(1 + np.abs(rew))
new_obs = StatePreprocessor.process(new_obs)
logger.log('{}/{} obs {} action {}'.format(
act_step_t, total_timesteps, obs, action))
act_step_t += 1
if len(new_obs) == 0:
done = True
else:
replay_buffer.add(obs, action, rew, new_obs, float(done))
obs = new_obs
# Post episode logging
summary = tf.Summary(value=[
tf.Summary.Value(tag="rewards",
simple_value=episode_rewards[-1])
])
summary_writer.add_summary(summary, act_step_t)
summary = tf.Summary(
value=[tf.Summary.Value(tag="eps", simple_value=update_eps)])
summary_writer.add_summary(summary, act_step_t)
summary = tf.Summary(value=[
tf.Summary.Value(tag="episode_steps",
simple_value=act_step_t - update_step_t)
])
summary_writer.add_summary(summary, act_step_t)
mean_5ep_reward = round(np.mean(episode_rewards[-5:]), 1)
num_episodes = len(episode_rewards)
if print_freq is not None and num_episodes % print_freq == 0:
logger.record_tabular("steps", act_step_t)
logger.record_tabular("episodes", num_episodes)
logger.record_tabular("mean 5 episode reward", mean_5ep_reward)
logger.record_tabular("% time spent exploring",
int(100 * exploration.value(act_step_t)))
logger.dump_tabular()
# Do the learning
start = time.time()
while update_step_t < min(act_step_t, total_timesteps):
if update_step_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(update_step_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
biases_t = pool.map(reward_shaper.get_action_potentials,
obses_t)
biases_tp1 = pool.map(reward_shaper.get_action_potentials,
obses_tp1)
td_errors, weighted_error = train(obses_t, biases_t,
actions, rewards,
obses_tp1, biases_tp1,
dones, weights)
# Loss logging
summary = tf.Summary(value=[
tf.Summary.Value(tag='weighted_error',
simple_value=weighted_error)
])
summary_writer.add_summary(summary, update_step_t)
if prioritized_replay:
new_priorities = np.abs(
td_errors) + prioritized_replay_eps
replay_buffer.update_priorities(
batch_idxes, new_priorities)
if update_step_t % target_network_update_freq == 0:
# Update target network periodically.
update_target()
update_step_t += 1
stop = time.time()
logger.log("Learning took {:.2f} seconds".format(stop - start))
if checkpoint_freq is not None and num_episodes % checkpoint_freq == 0:
# Periodically save the model and the replay buffer
rec_model_file = os.path.join(
td, "model_{}_{:.2f}".format(num_episodes,
mean_5ep_reward))
save_variables(rec_model_file)
buffer_file = os.path.join(
td, "buffer_{}_{}".format(num_episodes, update_step_t))
with open(buffer_file, 'wb') as foutput:
cloudpickle.dump(replay_buffer, foutput)
# Check whether it is best
if saved_mean_reward is None or mean_5ep_reward > saved_mean_reward:
if print_freq is not None:
logger.log(
"Saving model due to mean reward increase: {} -> {}"
.format(saved_mean_reward, mean_5ep_reward))
save_variables(model_file)
model_saved = True
saved_mean_reward = mean_5ep_reward
if model_saved:
if print_freq is not None:
logger.log("Restored model with mean reward: {}".format(
saved_mean_reward))
load_variables(model_file)
return act
def __init__(self, replay_dir):
self.replay_dir = replay_dir
self.state_preprocessor = StatePreprocessor()
self.demos = []
class ReplayRewardShaper:
""" Provides potential-based reward shaping based on expert demonstrations.
Uses replays to parse demonstrated state-action pairs and provides rewards
based on them.
Reference paper: https://www.ijcai.org/Proceedings/15/Papers/472.pdf.
"""
def __init__(self, replay_dir):
self.replay_dir = replay_dir
self.state_preprocessor = StatePreprocessor()
self.demos = []
def load(self):
for name in os.listdir(self.replay_dir):
dump_path = os.path.join(self.replay_dir, name)
with open(dump_path, 'rb') as dump_file:
replay = pickle.load(dump_file)
demo = self.__process_replay(replay)
self.demos.append(demo)
def __process_replay(self, replay):
demo = []
for i in range(len(replay) - 1):
state0, action_state0 = replay[i]
state1, _ = replay[i + 1]
state0 = state0[STATE_PROJECT]
state1 = state1[STATE_PROJECT]
if action_state0[0] == 1:
# attack the nearest creep
action = ATTACK_CREEP
elif action_state0[1] == 1:
# attack the enemy hero
action = ATTACK_HERO
elif action_state0[2] == 1:
# attack the enemy tower
action = ATTACK_TOWER
else:
# try to move
diff = state1[:2] - state0[:2]
if np.linalg.norm(diff) == 0:
# position did not change; skip transition
continue
angle_pi = math.atan2(diff[1], diff[0])
if angle_pi < 0:
angle_pi += 2 * math.pi
degrees = angle_pi / math.pi * 180
action = round(degrees / (360 / MOVES_TOTAL)) % MOVES_TOTAL
demo.append((self.state_preprocessor.process(state0), action,
self.state_preprocessor.process(state1)))
return demo
def get_potential(self, state, action):
best_value = 0
for demo in self.demos:
for demo_state, demo_action, _ in demo:
if demo_action != action:
continue
diff = state - demo_state
value = K * math.e**(-1 / 2 * diff.dot(SIGMA).dot(diff))
if value > best_value:
best_value = value
return best_value
def get_potentials(self, states, actions):
N = states.shape[0]
potentials = np.zeros(N)
for i in range(N):
potentials[i] = self.get_potential(states[i], actions[i])
return potentials
def get_nearest_demo(self, state):
best_norm = None
action = None
for demo in self.demos:
for demo_state, demo_action, _ in demo:
diff = state - demo_state
norm = np.linalg.norm(diff)
if action is None or norm < best_norm:
best_norm = norm
action = demo_action
return action