def test_header(self):
"""Define the arena with one version"""
arena = Arena([("Random A", lambda seed: AgentRandom(seed)),
("Random C", lambda seed: AgentRandom(seed)),
("Random B", lambda seed: AgentRandom(seed))], 5)
self.assertListEqual(arena.csv_header(),
["opponent", "Random A", "Random B", "Random C"])
def test_init_multiple(self):
"""Define the arena with several agents"""
arena = Arena([("Random A", lambda seed: AgentRandom(seed)),
("Random C", lambda seed: AgentRandom(seed)),
("Random B", lambda seed: AgentRandom(seed))], 5)
self.assertListEqual(arena.names(),
["Random A", "Random B", "Random C"])
def test_random_move_02(self):
"""Test multiple moves"""
game = Game()
agent = AgentRandom(454)
move = agent.move(game)
self.assertEqual(move, 5)
game.move(move)
move = agent.move(game)
self.assertEqual(move, 12)
def test_list(self):
"""Define the arena with one version"""
arena = Arena([("Random A", lambda seed: AgentRandom(seed)),
("Random C", lambda seed: AgentRandom(seed)),
("Random B", lambda seed: AgentRandom(seed))], 5)
self.assertEqual(len(arena.csv_results_lists()), 3)
self.assertListEqual(
arena.csv_results_lists(),
[['Random A', 0.4, 0.4, 0.4], ['Random C', 0.4, 0.4, 0.4],
['Random B', 0.4, 0.4, 0.4]])
def test_init_multiple(self):
"""Define the arena with several agents"""
arena = Arena([("Random A", lambda seed: AgentRandom(seed)),
("Random C", lambda seed: AgentRandom(seed)),
("Random B", lambda seed: AgentRandom(seed))], 5)
results = arena.results()
self.assertEqual(len(results), 3)
self.assertListEqual(
results, [['Random A', 0.4, 0.4, 0.4], ['Random C', 0.4, 0.4, 0.4],
['Random B', 0.4, 0.4, 0.4]])
PARSER = argparse.ArgumentParser(
description='Run the arena with availabe agents')
PARSER.add_argument('--output', type=str, default='arena.results.csv',
help='Path to write arena results')
ARGS = PARSER.parse_args()
print('Starting arena')
agents = [
# Place agents in this list as created
# first in the tuple is the readable name
# second is a lambda that ONLY takes a random seed. This can be discarded
# if the the Agent does not require a seed
("Random", lambda seed: AgentRandom(seed)),
('Max', lambda seed: AgentMax(seed)),
('Exact', lambda seed: AgentExact(seed)),
('MinMax', lambda seed: AgentMinMax(seed, depth=3))
]
if AGENT_A3C is not None:
agents.append(AGENT_A3C)
ARENA = Arena(agents, 500)
print('Run the arena for: ', ARENA.csv_header())
with open(ARGS.output, 'w') as f:
WRITER = csv.writer(f)
WRITER.writerow(ARENA.csv_header())
import torch
from mancala.agents.a3c import AgentA3C
dtype = torch.cuda.FloatTensor if torch.cuda.is_available(
) else torch.FloatTensor
AGENT_A3C = AgentA3C(os.path.join("models", "a3c.model"), dtype, 454)
except ImportError:
AGENT_A3C = None
except:
print("Unable to load A3C")
AGENT_A3C = None
FLASKAPP = Flask(__name__)
FLASKAPP.config.from_object(__name__)
# Define agents
AGENT_RANDOM = AgentRandom(454)
AGENT_MAX = AgentMax(454)
AGENT_MINNY = AgentMinMax(454, 3)
AGENT_EXACT = AgentExact(454)
def board_str_to_game(board, player_turn):
"""Turns parameters into game or error tuple"""
board_arr = split_string(board, 2)
if len(board_arr) != 14:
return jsonify({"error": "Invalid Board"}), 400
if player_turn != 1 and player_turn != 2:
return jsonify({"error": "Invalid Player"}), 400
def test_random_no_moves_player2(self):
"""Test move is seeded properly"""
move = AgentRandom(454).move(
Game([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], 2))
self.assertEqual(move, 0)
def test_random_move_seed(self):
"""Test move is seeded properly"""
move = AgentRandom(454).move(Game())
self.assertEqual(move, 5)
def test_random_move(self):
"""Test move picks a random spot on the right side"""
move = AgentRandom().move(Game())
self.assertEqual(move, 2)
def test_init_single(self):
"""Define the arena with one version"""
arena = Arena([("Random", lambda seed: AgentRandom(seed))], 5)
results = arena.results()
self.assertEqual(len(results), 1)
self.assertListEqual(results, [["Random", 0.4]])
def test_init_single(self):
"""Define the arena with one version"""
arena = Arena([("Random", lambda seed: AgentRandom(seed))], 5)
self.assertListEqual(arena.names(), ["Random"])
def test(rank, args, shared_model, dtype):
test_ctr = 0
torch.manual_seed(args.seed + rank)
# set up logger
timestring = str(date.today()) + '_' + \
time.strftime("%Hh-%Mm-%Ss", time.localtime(time.time()))
run_name = args.save_name + '_' + timestring
configure("logs/run_" + run_name, flush_secs=5)
env = MancalaEnv(args.seed + rank)
env.seed(args.seed + rank)
np_random, _ = seeding.np_random(args.seed + rank)
state = env.reset()
model = ActorCritic(state.shape[0], env.action_space).type(dtype)
model.eval()
state = torch.from_numpy(state).type(dtype)
reward_sum = 0
max_reward = -99999999
max_winrate = 0
rewards_recent = deque([], 100)
done = True
start_time = time.time()
last_test = time.time()
episode_length = 0
while True:
episode_length += 1
# Sync with the shared model
if done:
model.load_state_dict(shared_model.state_dict())
cx = Variable(torch.zeros(1, 400).type(dtype), volatile=True)
hx = Variable(torch.zeros(1, 400).type(dtype), volatile=True)
else:
cx = Variable(cx.data.type(dtype), volatile=True)
hx = Variable(hx.data.type(dtype), volatile=True)
value, logit, (hx, cx) = model((Variable(state.unsqueeze(0),
volatile=True), (hx, cx)))
prob = F.softmax(logit)
action = prob.max(1)[1].data.cpu().numpy()
scores = [(action, score)
for action, score in enumerate(prob[0].data.tolist())]
valid_actions = [action for action, _ in scores]
valid_scores = np.array([score for _, score in scores])
final_move = np_random.choice(valid_actions,
1,
p=valid_scores / valid_scores.sum())[0]
state, reward, done, _ = env.step(final_move)
done = done or episode_length >= args.max_episode_length
reward_sum += reward
if done:
rewards_recent.append(reward_sum)
rewards_recent_avg = sum(rewards_recent) / len(rewards_recent)
print(
"{} | {} | Episode Reward {: >4}, Length {: >2} | Avg Reward {:0.2f}"
.format(
datetime.datetime.now().isoformat(),
time.strftime("%Hh %Mm %Ss",
time.gmtime(time.time() - start_time)),
round(reward_sum, 2), episode_length,
round(rewards_recent_avg, 2)))
# if not stuck or args.evaluate:
log_value('Reward', reward_sum, test_ctr)
log_value('Reward Average', rewards_recent_avg, test_ctr)
log_value('Episode length', episode_length, test_ctr)
if reward_sum >= max_reward or \
time.time() - last_test > 60 * 8 or \
(len(rewards_recent) > 12 and
time.time() - last_test > 60 * 2 and
sum(list(rewards_recent)[-5:]) > sum(list(rewards_recent)[-10:-5])):
# if the reward is better or every 15 minutes
last_test = time.time()
max_reward = reward_sum if reward_sum > max_reward else max_reward
path_output = args.save_name + '_max'
torch.save(shared_model.state_dict(), path_output)
path_now = "{}_{}".format(args.save_name,
datetime.datetime.now().isoformat())
torch.save(shared_model.state_dict(), path_now)
win_rate_v_random = Arena.compare_agents_float(
lambda seed: AgentA3C(path_output, dtype, seed),
lambda seed: AgentRandom(seed), performance_games)
win_rate_v_exact = Arena.compare_agents_float(
lambda seed: AgentA3C(path_output, dtype, seed),
lambda seed: AgentExact(seed), performance_games)
win_rate_v_minmax = Arena.compare_agents_float(
lambda seed: AgentA3C(path_output, dtype, seed),
lambda seed: AgentMinMax(seed, 3), performance_games)
win_rate_exact_v = 1 - Arena.compare_agents_float(
lambda seed: AgentExact(seed),
lambda seed: AgentA3C(path_output, dtype, seed),
performance_games)
win_rate_minmax_v = 1 - Arena.compare_agents_float(
lambda seed: AgentMinMax(seed, 3),
lambda seed: AgentA3C(path_output, dtype, seed),
performance_games)
msg = " {} | Random: {: >5}% | Exact: {: >5}%/{: >5}% | MinMax: {: >5}%/{: >5}%".format(
datetime.datetime.now().strftime("%c"),
round(win_rate_v_random * 100, 2),
round(win_rate_v_exact * 100, 2),
round(win_rate_exact_v * 100, 2),
round(win_rate_v_minmax * 100, 2),
round(win_rate_minmax_v * 100, 2))
print(msg)
log_value('WinRate_Random', win_rate_v_random, test_ctr)
log_value('WinRate_Exact', win_rate_v_exact, test_ctr)
log_value('WinRate_MinMax', win_rate_v_minmax, test_ctr)
log_value('WinRate_ExactP2', win_rate_exact_v, test_ctr)
log_value('WinRate_MinMaxP2', win_rate_minmax_v, test_ctr)
avg_win_rate = (win_rate_v_exact + win_rate_v_minmax +
win_rate_exact_v + win_rate_minmax_v) / 4
if avg_win_rate > max_winrate:
print("Found superior model at {}".format(
datetime.datetime.now().isoformat()))
torch.save(
shared_model.state_dict(), "{}_{}_best_{}".format(
args.save_name,
datetime.datetime.now().isoformat(), avg_win_rate))
max_winrate = avg_win_rate
reward_sum = 0
episode_length = 0
state = env.reset()
test_ctr += 1
if test_ctr % 10 == 0 and not args.evaluate:
# pickle.dump(shared_model.state_dict(), open(args.save_name + '.p', 'wb'))
torch.save(shared_model.state_dict(), args.save_name)
if not args.evaluate:
time.sleep(60)
elif test_ctr == evaluation_episodes:
# Ensure the environment is closed so we can complete the
# submission
env.close()
# gym.upload('monitor/' + run_name, api_key=api_key)
state = torch.from_numpy(state).type(dtype)