def test_dqn_n_step(self):
obs = [1, 2, 3, 4, 5, 6, 7]
actions = ["a", "b", "a", "a", "a", "b", "a"]
rewards = [10.0, 0.0, 100.0, 100.0, 100.0, 100.0, 100.0]
new_obs = [2, 3, 4, 5, 6, 7, 8]
dones = [0, 0, 0, 0, 0, 0, 1]
_adjust_nstep(3, 0.9, obs, actions, rewards, new_obs, dones)
self.assertEqual(obs, [1, 2, 3, 4, 5, 6, 7])
self.assertEqual(actions, ["a", "b", "a", "a", "a", "b", "a"])
self.assertEqual(new_obs, [4, 5, 6, 7, 8, 8, 8])
self.assertEqual(dones, [0, 0, 0, 0, 1, 1, 1])
self.assertEqual(rewards,
[91.0, 171.0, 271.0, 271.0, 271.0, 190.0, 100.0])
def postprocess_trajectory(self,
sample_batch,
other_agent_batches=None,
episode=None):
# FIXME: Get done from info is required since agentwise done is not
# supported now.
sample_batch.data["dones"] = self.get_done_from_info(
sample_batch.data["infos"])
# N-step Q adjustments
if self.config["n_step"] > 1:
_adjust_nstep(self.config["n_step"], self.config["gamma"],
sample_batch[SampleBatch.CUR_OBS],
sample_batch[SampleBatch.ACTIONS],
sample_batch[SampleBatch.REWARDS],
sample_batch[SampleBatch.NEXT_OBS],
sample_batch[SampleBatch.DONES])
return sample_batch