def test_rand_vs_rand(self):
args1 = utils.hyperparameters(['--size=5', '--model=rand'])
args2 = utils.hyperparameters(['--size=5', '--model=rand'])
win_rate, _, _ = utils.multi_proc_play(args1,
args2,
self.num_games,
workers=8)
self.assertAlmostEqual(win_rate, 0.5, delta=0.1)
def test_mctac_vs_ac(self):
args1 = utils.hyperparameters(
['--size=9', '--model=ac', '--mcts=81', '--baseline'])
args2 = utils.hyperparameters(
['--size=9', '--model=ac', '--mcts=0', '--baseline'])
win_rate, _, _ = utils.multi_proc_play(args1,
args2,
self.num_games,
workers=8)
self.assertGreaterEqual(win_rate, 0.6)
def test_mctval_vs_val(self):
args1 = utils.hyperparameters(
['--size=7', '--mcts=128', '--temp=1', '--baseline'])
args2 = utils.hyperparameters(
['--size=7', '--mcts=0', '--temp=0.05', '--baseline'])
win_rate, _, _ = utils.multi_proc_play(args1,
args2,
self.num_games,
workers=8)
self.assertGreaterEqual(win_rate, 0.6)
def test_mct_vs_rand(self):
args1 = utils.hyperparameters([
'--size=5', '--model=greedy', '--mcts=10', '--temp=1', '--baseline'
])
args2 = utils.hyperparameters(['--size=5', '--model=rand'])
win_rate, _, _ = utils.multi_proc_play(args1,
args2,
self.num_games,
workers=8)
self.assertGreaterEqual(win_rate, 0.6)
def test_checkpoint(self):
args1 = utils.hyperparameters([
'--size=5', '--depth=0', '--temp=0.1',
'--customdir=bin/checkpoints/2020-01-20/'
])
args2 = utils.hyperparameters(
['--size=5', '--depth=0', '--temp=0.1', '--baseline'])
win_rate, _, _ = utils.multi_proc_play(args1,
args2,
self.num_games,
workers=8)
self.assertGreaterEqual(win_rate, 0.6)
def test_smartgreed_vs_greed(self):
args1 = utils.hyperparameters([
'--size=5', '--model=smartgreedy', '--depth=0', '--temp=0.1',
'--baseline'
])
args2 = utils.hyperparameters([
'--size=5', '--model=greedy', '--depth=0', '--temp=0.1',
'--baseline'
])
win_rate, _, _ = utils.multi_proc_play(args1,
args2,
self.num_games,
workers=8)
self.assertGreaterEqual(win_rate, 0.6)
def test_greedy_obvious_move(self):
args = utils.hyperparameters(['--size=5', '--model=greedy'])
self.set_obvious_move_state()
# Obvious move is to pass and win the game
policy, _ = utils.baselines.create_policy(args)
pi = policy(self.env)
self.assertEqual(pi[-1], 1)
self.assertTrue(np.allclose(pi[:-1], 0))
import gym
import go_ai.policies.baselines
from go_ai import game, utils
args = utils.hyperparameters(['customdir=bin/baselines/'])
# Environment
go_env = gym.make('gym_go:go-v0', size=args.size)
# Policies
policy, model = go_ai.policies.baselines.create_policy(args, 'Checkpoint')
print(f"Loaded model {policy}")
human_pi = go_ai.policies.baselines.Human(args.render)
# Play
go_env.reset()
game.pit(go_env, policy, human_pi)
go_env.reset()
for a in traj.actions:
greedy_actions = []
for policy in policies:
pi = policy(go_env)
greedy_actions.append(np.argmax(pi))
all_greedy_actions.append(greedy_actions)
go_env.step(a)
queue.put(np.array(all_greedy_actions, dtype=int))
if __name__ == '__main__':
all_args = []
for arg_str in arg_strs:
args = utils.hyperparameters(arg_str.split())
all_args.append(args)
_, _, replay = utils.multi_proc_play(all_args[0], all_args[0], 16)
greedy_actions = evaluate_greedy_actions(all_args, replay)
best_actions = greedy_actions[:, -1]
stats_str = ''
for i in range(len(all_args) - 1):
agreement = np.mean(greedy_actions[:, i] == best_actions)
stats_str += (f'{100 * agreement:.1f}% ')
print(stats_str)
import os
import gym
from go_ai.policies import actorcritic, baselines, value
import go_ai.search.plot
from go_ai import measurements, utils
utils.config_log()
args = utils.hyperparameters()
# Environment
go_env = gym.make('gym_go:go-v0', size=args.size)
if args.baseline:
outdir = 'bin/baselines/'
else:
outdir = args.customdir
# Policies
policy, model = baselines.create_policy(args, 'Model')
# Directories and files
plotsdir = os.path.join(outdir, f'{args.model}{args.size}_plots/')
if not os.path.exists(plotsdir):
os.mkdir(plotsdir)
stats_path = os.path.join(outdir, f'{args.model}{args.size}_stats.txt')
# Plot stats
if os.path.exists(stats_path):
