def run(self, num_epochs=1, num_episodes=1):
for ei in range(num_epochs):
task_performance = np.zeros(self.num_tasks)
for ti in range(self.num_tasks):
task_performance[ti] = expected_reward_tabular_normalized(self.dqn, self.tasks[ti], tol=1e-4)
ti = np.argmin(task_performance)
task = self.tasks[ti]
self.last_task_ti = ti
# (TODO) this breaks away the abstraction.
self.deepQlearn.task = task
self.dqn.task = task
# run training.
self.deepQlearn.run(num_episodes, task)
def run(self, num_epochs=1, num_episodes=1):
cov_func = lambda task1, task2, t1, t2: self.gpt_v * np.exp(- (self.dist(task1, task2) ** 2 * self.gpt_r + self.gpt_eta * (t1 - t2) ** 2))
for ei in range(num_epochs):
# task selection.
# complexity max(#task * history, history ** 2.3)
if len(self.examples) == 0: # no prior experience, choose randomly.
task = prob.choice(self.tasks, 1)[0]
else:
# GP-t.
mu = np.zeros(self.num_tasks)
sigma = np.zeros(self.num_tasks)
ucb = np.zeros(self.num_tasks)
# Kinv = npla.inv(self.K + self.gpt_sigma ** 2)
# Kinv_y = np.dot(Kinv, self.y)
Kinv_y = npla.solve(self.K + np.eye(self.t) * self.gpt_sigma ** 2, self.y)
for ti, task in enumerate(self.tasks):
vec = np.zeros(self.t)
for ei in range(self.t):
(t_ei, task_ei, _) = self.examples[ei]
vec[ei] = cov_func(task, task_ei, self.t, t_ei)
mu[ti] = np.dot(vec, Kinv_y)
Kinv_vec = npla.solve(self.K + np.eye(self.t) * self.gpt_sigma ** 2, vec)
sigma[ti] = self.gpt_v + self.gpt_sigma ** 2 - np.dot(vec, Kinv_vec)
ucb[ti] = mu[ti] + self.gpt_kappa * sigma[ti]
best_ti = np.argmax(ucb)
task = self.tasks[best_ti]
# store information for diagnosis.
self.mu = mu
self.sigma = sigma
self.ucb = ucb
# import pdb; pdb.set_trace()
# run training.
self._run_task(task, num_episodes=num_episodes)
# evaluate performance.
self.last_task_performance = np.zeros(self.num_tasks)
for ti in range(self.num_tasks):
self.last_task_performance[ti] = expected_reward_tabular_normalized(self.dqn, self.tasks[ti], tol=1e-4)
performance = np.mean(self.last_task_performance)
progress = performance - self.last_performance
# update statistics.
self.examples.append((self.t, task, progress))
self.t += 1
t = self.t
new_K = np.zeros((t, t))
new_y = np.zeros(t)
if t > 1:
new_K[:t - 1, :t - 1] = self.K
new_y[:t - 1] = self.y
new_K[t - 1, t - 1] = self.gpt_v
new_y[t - 1] = progress
for ei in range(t - 1):
(t_ei, task_ei, _) = self.examples[ei]
new_K[t - 1, ei] = cov_func(task_ei, task, t_ei, t - 1)
new_K[ei, t - 1] = new_K[t - 1, ei] # symmetric.
self.K = new_K
self.y = new_y
self.last_performance = performance
self.last_progress = progress
self.last_task = task
self.last_task_ti = self.tasks.index(task)