def act(self, step_wrappers):
# """""""""""""""""""""
# Act
# """""""""""""""""""""
SeatActorBase.act_mixed(step_wrappers=step_wrappers,
owner=self.seat_id,
br_learner=self.br_learner,
avg_learner=self.avg_learner,
current_policy_tags=self._current_policy_tags,
random_prob=self.br_learner.eps)
# """""""""""""""""""""
# Add to memories
# """""""""""""""""""""
for sw in step_wrappers:
e_i = sw.env_idx
if (self._current_policy_tags[e_i] == SeatActorBase.BR) and (
self._t_prof.add_random_actions_to_buffer or
(not sw.action_was_random)):
self._avg_buf_savers[e_i].add_step(
pub_obs=sw.obs,
a=sw.action,
legal_actions_mask=rl_util.get_legal_action_mask_np(
n_actions=self._env_bldr.N_ACTIONS,
legal_actions_list=sw.legal_actions_list))
self._br_memory_savers[e_i].add_experience(
obs_t_before_acted=sw.obs,
a_selected_t=sw.action,
legal_actions_list_t=sw.legal_actions_list)
def act_for_br_opp(self, step_wrappers):
""" Anticipatory; greedy BR + AVG """
SeatActorBase.act_mixed(
step_wrappers=step_wrappers,
br_learner=self.br_learner,
owner=self.owner,
avg_learner=self.avg_learner,
current_policy_tags=self._current_policy_tags_OPP_BR,
random_prob=0)
def act_for_avg_opp(self, step_wrappers):
"""
Purely random because that's how it should be for correct reach
"""
SeatActorBase.act_mixed(
step_wrappers=step_wrappers,
br_learner=self.br_learner,
owner=self.owner,
avg_learner=self.avg_learner,
current_policy_tags=self._current_policy_tags_O_AVG,
explore=True)
def act_for_br_trav(self, step_wrappers):
# Act
SeatActorBase.act_mixed(
step_wrappers=step_wrappers,
br_learner=self.br_learner,
owner=self.owner,
avg_learner=self.avg_learner,
current_policy_tags=self._current_policy_tags_T_BR,
random_prob=self._constant_eps)
# Add to memories
for sw in step_wrappers:
e_i = sw.env_idx
self._br_memory_savers[e_i].add_experience(
obs_t_before_acted=sw.obs,
a_selected_t=sw.action,
legal_actions_list_t=sw.legal_actions_list)