def __init__(self, args, shared_damped_model, global_count,
global_writer_loss_count, global_writer_quality_count,
global_win_event_count, save_dir):
super(AgentOffpac, self).__init__()
self.args = args
self.shared_damped_model = shared_damped_model
self.global_count = global_count
self.global_writer_loss_count = global_writer_loss_count
self.global_writer_quality_count = global_writer_quality_count
self.global_win_event_count = global_win_event_count
self.writer_idx_warmup_loss = 0
# self.eps = self.args.init_epsilon
self.save_dir = save_dir
if args.stop_qual_rule == 'naive':
self.stop_qual_rule = NaiveDecay(initial_eps=args.init_stop_qual,
episode_shrinkage=1,
change_after_n_episodes=5)
elif args.stop_qual_rule == 'gaussian':
self.stop_qual_rule = GaussianDecay(args.stop_qual_final,
args.stop_qual_scaling,
args.stop_qual_offset,
args.T_max)
else:
self.stop_qual_rule = NaiveDecay(args.init_stop_qual)
if self.args.eps_rule == "treesearch":
self.eps_rule = ActionPathTreeNodes()
elif self.args.eps_rule == "sawtooth":
self.eps_rule = ExpSawtoothEpsDecay()
elif self.args.eps_rule == 'gaussian':
self.eps_rule = GaussianDecay(args.eps_final, args.eps_scaling,
args.eps_offset, args.T_max)
else:
self.eps_rule = NaiveDecay(self.eps, 0.00005, 1000, 1)
def __init__(self, args, shared_average_model, global_count,
global_writer_loss_count, global_writer_quality_count,
global_win_event_count, save_dir):
super(AgentAcerContinuousTrainer, self).__init__()
self.args = args
self.shared_average_model = shared_average_model
self.global_count = global_count
self.global_writer_loss_count = global_writer_loss_count
self.global_writer_quality_count = global_writer_quality_count
self.global_win_event_count = global_win_event_count
self.writer_idx_warmup_loss = 0
# self.eps = self.args.init_epsilon
self.save_dir = save_dir
if args.stop_qual_rule == 'naive':
self.stop_qual_rule = NaiveDecay(initial_eps=args.init_stop_qual,
episode_shrinkage=1,
change_after_n_episodes=5)
elif args.stop_qual_rule == 'gaussian':
self.stop_qual_rule = GaussianDecay(args.stop_qual_final,
args.stop_qual_scaling,
args.stop_qual_offset,
args.T_max)
elif args.stop_qual_rule == 'running_average':
self.stop_qual_rule = RunningAverage(
args.stop_qual_ra_bw,
args.stop_qual_scaling + args.stop_qual_offset,
args.stop_qual_ra_off)
else:
self.stop_qual_rule = NaiveDecay(args.init_stop_qual)
if self.args.eps_rule == "treesearch":
self.b_sigma_rule = ActionPathTreeNodes()
elif self.args.eps_rule == "sawtooth":
self.b_sigma_rule = ExpSawtoothEpsDecay()
elif self.args.eps_rule == 'gaussian':
self.b_sigma_rule = GaussianDecay(args.b_sigma_final,
args.b_sigma_scaling,
args.p_sigma, args.T_max)
elif self.args.eps_rule == "self_reg_min":
self.args.T_max = np.inf
self.b_sigma_rule = FollowLeadMin(
(args.stop_qual_scaling + args.stop_qual_offset), 1)
elif self.args.eps_rule == "self_reg_avg":
self.args.T_max = np.inf
self.b_sigma_rule = FollowLeadAvg(
(args.stop_qual_scaling + args.stop_qual_offset) / 4, 2, 1)
elif self.args.eps_rule == "self_reg_exp_avg":
self.args.T_max = np.inf
self.b_sigma_rule = ExponentialAverage(
(args.stop_qual_scaling + args.stop_qual_offset) / 4, 0.9, 1)
else:
self.b_sigma_rule = NaiveDecay(self.eps, 0.00005, 1000, 1)
def __init__(self, cfg, args, global_count, global_writer_loss_count,
global_writer_quality_count, global_win_event_count,
action_stats_count, save_dir):
super(AgentSacTrainer_sg_lg, self).__init__()
self.cfg = cfg
self.args = args
self.global_count = global_count
self.global_writer_loss_count = global_writer_loss_count
self.global_writer_quality_count = global_writer_quality_count
self.global_win_event_count = global_win_event_count
self.action_stats_count = action_stats_count
# self.eps = self.args.init_epsilon
self.save_dir = save_dir
if args.stop_qual_rule == 'naive':
self.stop_qual_rule = NaiveDecay(initial_eps=args.init_stop_qual,
episode_shrinkage=1,
change_after_n_episodes=5)
elif args.stop_qual_rule == 'gaussian':
self.stop_qual_rule = GaussianDecay(args.stop_qual_final,
args.stop_qual_scaling,
args.stop_qual_offset,
args.T_max)
elif args.stop_qual_rule == 'running_average':
self.stop_qual_rule = RunningAverage(
args.stop_qual_ra_bw,
args.stop_qual_scaling + args.stop_qual_offset,
args.stop_qual_ra_off)
else:
self.stop_qual_rule = Constant(args.stop_qual_final)
if self.cfg.temperature_regulation == 'follow_quality':
self.beta_rule = FollowLeadAvg(1, 80, 1)
elif self.cfg.temperature_regulation == 'constant':
self.eps_rule = Constant(cfg.init_temperature)