def new_tp(i):
TP = TrainingProfile(name=exp_name,
nn_type="feedforward",
eval_agent_export_freq=3,
checkpoint_freq=3,
max_buffer_size_adv=1e6,
n_traversals_per_iter=500,
n_batches_adv_training=250,
mini_batch_size_adv=2048,
game_cls=StandardLeduc,
n_units_final_adv=64,
n_merge_and_table_layer_units_adv=64,
init_adv_model="random", # warm start neural weights with init from last iter
use_pre_layers_adv=False, # shallower nets
use_pre_layers_avrg=False, # shallower nets
# You can specify one or both modes. Choosing both is useful to compare them.
eval_modes_of_algo=(
EvalAgentDeepCFR.EVAL_MODE_SINGLE, # SD-CFR
),
DISTRIBUTED=True,
log_verbose=True,
rl_br_args=RLBRArgs(rlbr_bet_set=None,
#n_hands_each_seat=5*i,
n_workers=5,
# Training
DISTRIBUTED=True,
n_iterations=1000*(i+1),
play_n_games_per_iter=50,
# The DDQN
batch_size=512,
)
#lbr_args = LBRArgs()
)
return TP
init_adv_model="random", # warm start neural weights with init from last iter
use_pre_layers_adv=False, # shallower nets
use_pre_layers_avrg=False, # shallower nets
# You can specify one or both modes. Choosing both is useful to compare them.
eval_modes_of_algo=(
EvalAgentDeepCFR.EVAL_MODE_SINGLE, # SD-CFR
),
DISTRIBUTED=True,
log_verbose=True,
rl_br_args=RLBRArgs(rlbr_bet_set=None,
n_hands_each_seat=200,
n_workers=1,
# Training
DISTRIBUTED=False,
n_iterations=100,
play_n_games_per_iter=50,
# The DDQN
batch_size=512,
),
lbr_args=LBRArgs(n_lbr_hands_per_seat=30000,
n_parallel_lbr_workers=10,
DISTRIBUTED=True,
),
),
eval_methods={'br': 1,
#'rlbr': 1,
'lbr': 1,
},
n_iterations=12)
ctrl.run()
use_pre_layers_avrg=True,
n_cards_state_units_avrg=192,
n_merge_and_table_layer_units_avrg=64,
n_units_final_avrg=64,
max_buffer_size_avrg=2e6,
lr_avrg=0.001,
lr_patience_avrg=99999999, # No lr decay
# With the H2H evaluator, these two are evaluated against eachother.
eval_modes_of_algo=(
EvalAgentDeepCFR.EVAL_MODE_AVRG_NET,
),
log_verbose=True,
game_cls=Flop5Holdem,
# enables simplified obs. Default works also for 3+ players
use_simplified_headsup_obs=True,
rl_br_args=RLBRArgs(rlbr_bet_set=POT_ONLY),
),
# Evaluate Head-to-Head every 15 iterations of both players (= every 30 alternating iterations)
eval_methods={},
# 150 = 300 when 2 viewing alternating iterations as 2 (as usually done).
# This repo implements alternating iters as a single iter, which is why this says 150.
n_iterations=10,
)
ctrl.run()
use_pre_layers_adv=True,
n_cards_state_units_adv=192,
n_merge_and_table_layer_units_adv=64,
n_units_final_adv=64,
max_buffer_size_adv=1e6, # *20 LAs = 40M
lr_adv=0.001,
lr_patience_adv=99999999, # No lr decay
# With the H2H evaluator, these two are evaluated against eachother.
eval_modes_of_algo=(
EvalAgentDeepCFR.EVAL_MODE_SINGLE,
),
log_verbose=True,
game_cls=Flop5Holdem,
# enables simplified obs. Default works also for 3+ players
use_simplified_headsup_obs=True,
rl_br_args=RLBRArgs(rlbr_bet_set=POT_ONLY, n_iterations=2000),
),
# Evaluate Head-to-Head every 15 iterations of both players (= every 30 alternating iterations)
eval_methods={'rlbr': 5},
# 150 = 300 when 2 viewing alternating iterations as 2 (as usually done).
# This repo implements alternating iters as a single iter, which is why this says 150.
n_iterations=50,
)
ctrl.run()