class SingleLearnerSequential(object):
def __init__(self, dqn, tasks, **kwargs):
self.dqn = dqn
self.tasks = tasks
self.num_tasks = len(self.tasks)
self.deepQlearn = DeepQlearn(tasks[0], dqn, **kwargs)
self.t = 0
def run(self, num_epochs=1, budget=100):
for ei in range(num_epochs):
ti = self.t % self.num_tasks
task = self.tasks[ti]
# (TODO) this breaks away the abstraction.
self.deepQlearn.task = task
self.dqn.task = task
# run training.
self.deepQlearn.run(budget, task)
self.t += 1
for it in range(budget):
self.deepQlearn._update_net()
