mode="max",
)
else:
best_checkpoint = "/home/jippo/ray_results/YanivTrainer_2021-03-28_13-22-27/YanivTrainer_yaniv_4258f_00000_0_2021-03-28_13-22-27/checkpoint_60/checkpoint_60/checkpoint-60"
agent = ppo.PPOTrainer(config=config, env="yaniv")
agent.restore(best_checkpoint)
rule_agent = YanivNoviceRuleAgent(single_step=True)
env = YanivEnv(env_config)
for _ in range(args.eval_num):
episode_reward = 0
done = {"__all__": False}
obs = env.reset()
agent_id = "player_0"
rules_id = "player_1"
steps = 0
while not done["__all__"]:
if env.current_player == 0:
action = agent.compute_action(obs[agent_id])
obs, reward, done, info = env.step({agent_id: action})
else:
state = env.game.get_state(1)
extracted_state = {}
extracted_state["raw_obs"] = state
extracted_state["raw_legal_actions"] = [
a for a in state["legal_actions"]
class YanivTournament:
def __init__(self, env_config, trainers=[], opponent="novice"):
self.env_config = env_config
self.trainers = trainers
if opponent == "novice":
self.rule_agent = YanivNoviceRuleAgent(
single_step=env_config.get("single_step", True))
elif opponent == "intermediate":
self.rule_agent = YanivIntermediateRuleAgent(
single_step=env_config.get("single_step", True))
else:
raise ValueError("opponent wrong {}".format(opponent))
self.env = YanivEnv(env_config)
self.players = []
for i in range(self.env.num_players):
if i < len(self.trainers):
self.players.append(self.trainers[i])
else:
self.players.append(self.rule_agent)
self.reset_stats()
def run_episode(self, render=False):
obs = self.env.reset()
if render:
self.env.game.render()
done = {"__all__": False}
states = [
t.get_policy("policy_1").model.get_initial_state()
for t in self.trainers
]
steps = 0
while not done["__all__"]:
player = self.players[self.env.current_player]
player_id = self.env.current_player_string
if player in self.trainers:
action, state, _ = player.compute_action(
obs[player_id],
policy_id="policy_1",
state=states[self.env.current_player],
full_fetch=True,
)
states[self.env.current_player] = state
if self.env.game.round.discarding:
dec_action = self.env._decode_action(action)
if dec_action != utils.YANIV_ACTION:
self.player_stats[player_id]["discard_freqs"][str(
int(len(dec_action) / 2))] += 1
else:
pickup_action = self.env._decode_action(action)
self.player_stats[player_id]["pickup_freqs"][
pickup_action] += 1
obs, reward, done, info = self.env.step({player_id: action})
else:
state = self.env.game.get_state(self.env.current_player)
extracted_state = {}
extracted_state["raw_obs"] = state
extracted_state["raw_legal_actions"] = [
a for a in state["legal_actions"]
]
action = self.rule_agent.step(extracted_state)
if self.env.game.round.discarding:
if action != utils.YANIV_ACTION:
self.player_stats[player_id]["discard_freqs"][str(
int(len(action) / 2))] += 1
else:
self.player_stats[player_id]["pickup_freqs"][action] += 1
obs, reward, done, info = self.env.step({player_id: action},
raw_action=True)
steps += 1
if render:
self.env.game.render()
self.game_stats["avg_roundlen"] += steps
winner = self.env.game.round.winner
if winner == -1:
self.game_stats["avg_draws"] += 1
else:
winner_id = self.env._get_player_string(winner)
self.player_stats[winner_id]["avg_wins"] += 1
self.player_stats[winner_id]["winning_hands"].append(
utils.get_hand_score(self.env.game.players[winner].hand))
assaf = self.env.game.round.assaf
if assaf is not None:
self.player_stats[self.env._get_player_string(
assaf)]["avg_assafs"] += 1
s = self.env.game.round.scores
if s is not None:
for i in range(self.env.num_players):
if s[i] > 0:
self.player_stats[self.env._get_player_string(
i)]["scores"].append(s[i])
self.games_played += 1
def reset_game(self):
self.scores = [[] for _ in range(self.env.num_players)]
def run_game(self):
self.reset_game()
self.run_episode()
def run(self, eval_num):
self.reset_stats()
for _ in range(eval_num):
self.run_episode()
return self.get_average_stats()
def reset_stats(self):
self.games_played = 0
self.game_stats = {
"avg_roundlen": 0,
"avg_draws": 0,
}
self.player_stats = {
player_id: {
"avg_wins": 0,
"avg_assafs": 0,
"scores": [],
"winning_hands": [],
"discard_freqs": {
"1": 0,
"2": 0,
"3": 0,
"4": 0,
"5": 0,
},
"pickup_freqs": {a: 0
for a in utils.pickup_actions},
}
for player_id in self.env._get_players()
}
def get_average_stats(self):
stats = {
"game": deepcopy(self.game_stats),
"player": deepcopy(self.player_stats),
}
for key in stats["game"].keys():
if key.startswith("avg"):
stats["game"][key] /= self.games_played
for player_stats in stats["player"].values():
for key in player_stats:
if key.startswith("avg"):
player_stats[key] /= self.games_played
player_stats["avg_losing_score"] = (np.mean(
player_stats["scores"]) if len(player_stats["scores"]) > 0 else
0)
player_stats.pop("scores")
player_stats["avg_winning_hand"] = (
np.mean(player_stats["winning_hands"])
if len(player_stats["winning_hands"]) > 0 else 0)
player_stats.pop("winning_hands")
return stats
def print_stats(self):
avg_stats = self.get_average_stats()
cleaned = json.dumps(avg_stats)
print(yaml.safe_dump(json.loads(cleaned), default_flow_style=False))