def test_from_mdp_lst_biased(self):
mdp_lst = [
OvercookedGridworld.from_layout_name(name)
for name in self.layout_name_short_lst
]
ae = AgentEvaluator.from_mdp_lst(mdp_lst=mdp_lst,
env_params={"horizon": 400},
sampling_freq=self.biased)
counts = {}
for _ in range(self.num_reset):
ae.env.reset(regen_mdp=True)
if ae.env.mdp.layout_name in counts:
counts[ae.env.mdp.layout_name] += 1
else:
counts[ae.env.mdp.layout_name] = 1
# construct the ground truth
gt = {
self.layout_name_short_lst[i]: self.biased[i]
for i in range(len(self.layout_name_short_lst))
}
for k, v in counts.items():
self.assertAlmostEqual(gt[k], v / self.num_reset, 2,
"more than 2 places off for " + k)
def test_from_mdp_lst_uniform(self):
mdp_lst = [OvercookedGridworld.from_layout_name(name) for name in self.layout_name_short_lst]
ae = AgentEvaluator.from_mdp_lst(mdp_lst=mdp_lst, env_params={"horizon": 400}, sampling_freq=[0.2, 0.2, 0.2, 0.2, 0.2])
counts = {}
for _ in range(self.num_reset):
ae.env.reset(regen_mdp=True)
if ae.env.mdp.layout_name in counts:
counts[ae.env.mdp.layout_name] += 1
else:
counts[ae.env.mdp.layout_name] = 1
for k, v in counts.items():
self.assertAlmostEqual(0.2, v/self.num_reset, 2, "more than 2 places off for " + k)