def grid_world1_sliding(exp_id=3, path="./Results/gridworld1"):
opt = {}
opt["exp_id"] = exp_id
opt["path"] = path
opt["checks_per_policy"] = 10
opt["max_steps"] = 150000
opt["num_policy_checks"] = 20
noise = 0.1
exp = 0.3
discretization = 400
maze = os.path.join(ConsumableGridWorld.default_map_dir, '10x7-ACC2011.txt')
domain = ConsumableGridWorldIRL([(7,5), (1,2)],
mapname=maze,
encodingFunction= lambda x: ConsumableGridWorldIRL.slidingWindowEncoding(x,3),
noise=noise)
opt["domain"] = domain
# Representation
representation = IncrementalTabular(domain, discretization=discretization)
# Policy
policy = eGreedy(representation, epsilon=exp)
# Agent
opt["agent"] = Q_Learning(representation=representation, policy=policy,
discount_factor=domain.discount_factor,
initial_learn_rate=0.1,
learn_rate_decay_mode="boyan", boyan_N0=100,
lambda_=0.)
experiment = Experiment(**opt)
experiment.run(visualize_steps=False,
visualize_learning=False,
visualize_performance=0)
experiment.save()
return np.max(experiment.result["return"]),np.sum(experiment.result["return"])
def createSlidingDomain(self,k):
return ConsumableGridWorldIRL(self.env_template["consumable"],
mapname=self.env_template["map"],
encodingFunction= lambda x: ConsumableGridWorldIRL.slidingWindowEncoding(x,k),
noise=self.env_template["noise"])
