def train(load_path):
env = LoveLetterMultiAgentEnv(num_players=4)
env.seed(SEED)
# take mujoco hyperparams (but doubled timesteps_per_actorbatch to cover more steps.)
# model = PPO(MlpPolicy, env, timesteps_per_actorbatch=4096, clip_param=0.2, entcoeff=0.0, optim_epochs=10,
# optim_stepsize=3e-4, optim_batchsize=64, gamma=0.99, lam=0.95, schedule='linear', verbose=2)
if load_path:
model = PPO.load(load_path, env)
else:
model = PPO(MlpPolicy, env)
random_agents = [RandomAgent(env, SEED + i) for i in range(3)]
agents = [model, *random_agents]
env.set_agents(agents)
eval_callback = EvalCallback(env,
best_model_save_path=LOGDIR,
log_path=LOGDIR,
eval_freq=EVAL_FREQ,
n_eval_episodes=EVAL_EPISODES)
model.learn(total_timesteps=NUM_TIMESTEPS, callback=eval_callback)
model.save(os.path.join(
LOGDIR, "final_model")) # probably never get to this point.
env.close()
def train(output_folder, load_path):
base_output = Path(output_folder)
full_output = base_output / datetime.datetime.now().isoformat(
timespec="seconds")
# latest = base_output / "latest"
# latest.symlink_to(full_output)
logger.configure(folder=str(full_output))
env = LoveLetterMultiAgentEnv(num_players=4,
reward_fn=Rewards.fast_elimination_reward)
env.seed(SEED)
# take mujoco hyperparams (but doubled timesteps_per_actorbatch to cover more steps.)
# model = PPO(MlpPolicy, env, timesteps_per_actorbatch=4096, clip_param=0.2, entcoeff=0.0, optim_epochs=10,
# optim_stepsize=3e-4, optim_batchsize=64, gamma=0.99, lam=0.95, schedule='linear', verbose=2)
if load_path:
model = PPO.load(load_path, env)
else:
# def test_fn(env):
# return env.valid_action_mask()
#
model = PPO(MlpPolicy, env, verbose=1,
ent_coef=0.05) #, action_mask_fn=test_fn)
other_agents = [RandomAgent(env, SEED + i) for i in range(3)]
# other_agents = [
# PPO.load("zoo/ppo_logging/2020-12-27T15:51:49/final_model", env),
# ]
# PPO.load("zoo/ppo_reward_bugfix2/latest/best_model", env),
# PPO.load("zoo/ppo_reward_bugfix2/latest/best_model", env),
# ]
agents = [model, *other_agents]
env.set_agents(agents)
eval_callback = EvalCallback(
env,
best_model_save_path=str(full_output),
log_path=str(full_output),
eval_freq=EVAL_FREQ,
n_eval_episodes=EVAL_EPISODES,
)
model.learn(total_timesteps=NUM_TIMESTEPS, callback=eval_callback)
model.save(str(full_output / "final_model"))
env.close()
def make_agents(env):
new_load_path = "zoo/ppo_kl/2020-12-27T16:28:42/final_model"
new_model = PPO.load(new_load_path, env)
old_load_path = "zoo/ppo_logging/2020-12-27T15:51:49/final_model"
# old_load_path = "zoo/ppo_headsup/latest/best_model"
old_model = PPO.load(old_load_path, env)
# random1 = RandomAgent(env)
# random2 = RandomAgent(env)
return [new_model, old_model]
env = LoveLetterMultiAgentEnv(num_players=2, make_agents_cb=make_agents)
GAME_LIMIT = 20000
STEP_LIMIT = 100
wins_by_pos = {p.position: 0 for p in env.players}
starts_by_pos = {p.position: 0 for p in env.players}
invalid_games = 0
for i in range(GAME_LIMIT):
if i % 100 == 0:
print(i)
print("Wins by position")
from gym_love_letter.interface.api import make_api
app = Flask(__name__, static_url_path="/static", static_folder="dist")
def make_agents(env):
human = HumanAgent()
# load_path = "zoo/ppo_reward_bugfix4/latest/best_model"
# load_path = "zoo/ppo_logging/2020-12-27T15:51:49/final_model"
# load_path = "zoo/ppo_kl/2020-12-27T16:28:42/final_model"
# model = PPO.load(load_path, env)
random1 = RandomAgent(env)
# random2 = RandomAgent(env)
return [human, random1] # model] # random1, random2]
env = LoveLetterMultiAgentEnv(num_players=2, make_agents_cb=make_agents)
@app.route('/')
def index():
return app.send_static_file("index.html")
api = make_api(env)
app.register_blueprint(api, url_prefix="/api")
# Launch the game
webbrowser.open("http://localhost:5000")