def save_best_response_checkpoint(trainer: DQNTrainer,
player: int,
save_dir: str,
timesteps_training_br: int,
episodes_training_br: int,
active_policy_num: int = None):
policy_name = active_policy_num if active_policy_num is not None else "unclaimed"
date_time = datetime_str()
checkpoint_name = f"policy_{policy_name}_{date_time}.h5"
checkpoint_path = os.path.join(save_dir, checkpoint_name)
br_weights = trainer.get_weights([f"best_response"])["best_response"]
br_weights = {k.replace(".", "_dot_"): v
for k, v in br_weights.items()
} # periods cause HDF5 NaturalNaming warnings
ensure_dir(file_path=checkpoint_path)
num_save_attempts = 5
for attempt in range(num_save_attempts):
try:
deepdish.io.save(path=checkpoint_path,
data={
"weights": br_weights,
"player": player,
"policy_num": active_policy_num,
"date_time_str": date_time,
"seconds_since_epoch": time.time(),
"timesteps_training_br":
timesteps_training_br,
"episodes_training_br": episodes_training_br
})
break
except HDF5ExtError:
if attempt + 1 == num_save_attempts:
raise
time.sleep(1.0)
return checkpoint_path
# dqn_policy: X
# ppo_policy: Y
for i in range(args.stop_iters):
print("== Iteration", i, "==")
# improve the DQN policy
print("-- DQN --")
result_dqn = dqn_trainer.train()
print(pretty_print(result_dqn))
# improve the PPO policy
print("-- PPO --")
result_ppo = ppo_trainer.train()
print(pretty_print(result_ppo))
# Test passed gracefully.
if args.as_test and \
result_dqn["episode_reward_mean"] > args.stop_reward and \
result_ppo["episode_reward_mean"] > args.stop_reward:
print("test passed (both agents above requested reward)")
quit(0)
# swap weights to synchronize
dqn_trainer.set_weights(ppo_trainer.get_weights(["ppo_policy"]))
ppo_trainer.set_weights(dqn_trainer.get_weights(["dqn_policy"]))
# Desired reward not reached.
if args.as_test:
raise ValueError("Desired reward ({}) not reached!".format(
args.stop_reward))