def self_play(ctx):
"""Train by self-play, retraining from self-played frames and changing best player when
new trained player beats currently best player.
Args:
ctx (click.core.Context): context object.
Parameters for training:
* 'game' (string) : game name (Default: tictactoe)
* 'max_iter' (int) : number of train process iterations
(Default: -1)
* 'min_examples' (int) : minimum number of examples to start training
nn, if -1 then no threshold. (Default: -1)
* 'policy_warmup' (int) : how many stochastic warmup steps should take
deterministic policy (Default: 12)
* 'n_self_plays' (int) : number of self played episodes
(Default: 100)
* 'n_tournaments' (int) : number of tournament episodes (Default: 20)
* 'save_checkpoint_folder' (string) : folder to save best models
(Default: "checkpoints")
* 'save_checkpoint_filename' (string) : filename of best model (Default: "best")
* 'save_self_play_log_path' (string) : where to save self-play logs.
(Default: "./logs/self-play.log")
* 'save_tournament_log_path' (string) : where to save tournament logs.
(Default: "./logs/tournament.log")
* 'update_threshold' (float): : required threshold to be new best player
(Default: 0.55)
"""
cfg = ctx.obj
coach = Coach(cfg)
# Create TensorBoard logger
tb_logger = TensorBoardLogger(
utils.create_tensorboard_log_dir(cfg.logging['tensorboard_log_folder'],
'score'))
iteration = coach.global_epoch // cfg.training['epochs']
while cfg.self_play[
"max_iter"] == -1 or iteration < cfg.self_play["max_iter"]:
iter_counter_str = "{:03d}/{:03d}".format(iteration + 1, cfg.self_play["max_iter"]) \
if cfg.self_play["max_iter"] > 0 else "{:03d}/inf".format(iteration + 1)
coach.play("Self-play " + iter_counter_str)
# Proceed to training only if threshold is fulfilled
if len(coach.storage.big_bag) <= cfg.self_play["min_examples"]:
log.warning("Skip training, gather minimum %d training examples!",
cfg.self_play["min_examples"])
continue
coach.train()
coach.evaluate("Tournament " + iter_counter_str, tournament_mode=True)
# Log current player's score
tb_logger.log_scalar("Best score", coach.best_score, iteration)
# Increment iterator
iteration += 1
def human_play(ctx, model_path, n_games):
"""Play `n_games` with trained model.
Args:
model_path: (string): Path to trained model.
"""
cfg = ctx.obj
coach = Coach(cfg, model_path)
coach.current_mind.players[1] = HumanPlayer(cfg.mdp)
coach.eval_callbacks.append(BoardRender(cfg.env, render=True, fancy=True))
coach.scoreboard = Tournament()
coach.evaluate(desc="Test models: Human vs. {}".format(
model_path.split("/")[-1]),
n_games=n_games)
log.info("Human vs. %s results: %s",
model_path.split("/")[-1], coach.scoreboard.results)
def clash(ctx, first_model_path, second_model_path, render, n_games):
"""Test two models. Play `n_games` between themselves.
Args:
first_model_path: (string): Path to player one model.
second_model_path (string): Path to player two model.
"""
cfg = ctx.obj
coach = Coach(cfg,
current_ckpt=first_model_path,
best_ckpt=second_model_path)
coach.scoreboard = Tournament()
coach.evaluate(desc="Test models: {} vs {}".format(
first_model_path.split("/")[-1],
second_model_path.split("/")[-1]),
render_mode=render,
n_games=n_games)
log.info("%s vs %s results: %s",
first_model_path.split("/")[-1],
second_model_path.split("/")[-1], coach.scoreboard.results)
