def test_single_game_term_t2():
t = 2
batch_size = 1
torch.manual_seed(123)
np.random.seed(123)
s = ecn.State(**sampling.generate_batch(batch_size))
s.pool = torch.LongTensor([[3, 7, 2]])
s.utilities = torch.LongTensor([[[5, 4, 3], [3, 4, 5]]])
s.last_proposal = torch.LongTensor([[3, 0, 0]])
total_available = 3 * 5 + 7 * 4 + 2 * 5
print('total_available', total_available)
# so, the proposer is the second agent, ie agent 1
# so, the proposer, agent 1, will take: 3 0 0
# accepter, agent 0, will take 0 7 2
actual = 0 * 5 + 7 * 4 + 2 * 3 + \
3 * 3
print('actual', actual)
ratio = actual / total_available
print('ratio', ratio)
agent = 0 if t % 2 == 0 else 1
term = torch.ByteTensor([1])
rewards = rewards_lib.calc_rewards(s=s, t=t, term=term)
assert rewards[0, 0] == approx(
(0 * 5 + 7 * 4 + 2 * 3) / (3 * 5 + 7 * 4 + 2 * 3))
assert rewards[0, 1] == approx(
(3 * 3 + 0 * 4 + 0 * 5) / (3 * 3 + 7 * 4 + 2 * 5))
assert rewards[0, 2] == approx(ratio)
def test_single_game_term_exceeds_withinpool2():
t = 1
batch_size = 1
torch.manual_seed(123)
np.random.seed(123)
s = ecn.State(**sampling.generate_batch(batch_size))
s.pool = torch.LongTensor([[3, 7, 2]])
s.utilities = torch.LongTensor([[[5, 4, 3], [3, 4, 5]]])
# last proposal means agent 0's, and we are now on agent 1, who is accepintg it
s.last_proposal = torch.LongTensor([[3, 7, 2]])
total_available = 3 * 5 + 7 * 4 + 2 * 5
print('total_available', total_available)
actual = 3 * 5 + 7 * 4 + 2 * 3
print('actual', actual)
ratio = actual / total_available
print('ratio', ratio)
agent = 0 if t % 2 == 0 else 1
term = torch.ByteTensor([1])
rewards = rewards_lib.calc_rewards(s=s, t=t, term=term)
assert rewards[0, 0] == approx(
(3 * 5 + 7 * 4 + 2 * 3) / (3 * 5 + 7 * 4 + 2 * 3))
assert rewards[0, 1] == approx((0) / (3 * 3 + 7 * 4 + 2 * 5))
assert rewards[0, 2] == approx(ratio)
def test_single_game_noterm():
t = 1
batch_size = 1
torch.manual_seed(123)
np.random.seed(123)
s = ecn.State(**sampling.generate_batch(batch_size))
agent = 0 if t % 2 == 0 else 1
term = torch.ByteTensor([0])
rewards = rewards_lib.calc_rewards(s=s, t=t, term=term)
assert rewards[0].tolist() == [0, 0, 0]
def test_rewards_t0():
t = 0
batch_size = 128
torch.manual_seed(123)
np.random.seed(123)
s = ecn.State(**sampling.generate_batch(batch_size))
agent = 0 if t % 2 == 0 else 1
term = torch.from_numpy(np.random.choice(2, batch_size)).long()
rewards = rewards_lib.calc_rewards(s=s, t=t, term=term)
assert rewards.size() == (batch_size, 3)
assert rewards.abs().sum() == 0
def test_rewards_t1():
t = 1
batch_size = 97
torch.manual_seed(123)
np.random.seed(123)
s = ecn.State(**sampling.generate_batch(batch_size))
agent = 0 if t % 2 == 0 else 1
term = torch.from_numpy(np.random.choice(2, batch_size)).long()
rewards = rewards_lib.calc_rewards(s=s, t=t, term=term)
# print('alive_games', alive_games)
for b in range(batch_size):
# game = alive_games[b]
assert rewards[b].tolist() == [0, 0, 0] or term[b] == 1
def test_single_game_term_t2_batch3():
t = 2
batch_size = 3
torch.manual_seed(123)
np.random.seed(123)
s = ecn.State(**sampling.generate_batch(batch_size))
s.pool = torch.from_numpy(np.random.choice(10, (batch_size, 3))).long()
s.pool[1] = torch.LongTensor([3, 7, 2])
s.utilities = torch.from_numpy(np.random.choice(
10, (batch_size, 2, 3))).long()
s.utilities[1] = torch.LongTensor([[5, 4, 3], [3, 4, 5]])
s.last_proposal = torch.from_numpy(np.random.choice(
10, (batch_size, 3))).long()
s.last_proposal[1] = torch.LongTensor([3, 0, 0])
term = torch.ByteTensor([0, 1, 0])
# since only one terminated, reward should be for simply the hard-coded ones above
# all others should be zero
s.pool[0] = s.last_proposal[0]
s.pool[2] = s.last_proposal[2]
# make rewards for 0 and 2 1.0
s.utilities[0][1] = torch.max(s.utilities[0], 0)[0].view(1, 3)
s.utilities[2][1] = torch.max(s.utilities[2], 0)[0].view(1, 3)
total_available = 3 * 5 + 7 * 4 + 2 * 5
print('total_available', total_available)
# so, the proposer is the second agent, ie agent 1
# so, the proposer, agent 1, will take: 3 0 0
# accepter, agent 0, will take 0 7 2
actual = 0 * 5 + 7 * 4 + 2 * 3 + \
3 * 3
print('actual', actual)
ratio = actual / total_available
print('ratio', ratio)
agent = 0 if t % 2 == 0 else 1
rewards = rewards_lib.calc_rewards(s=s, t=t, term=term)
assert rewards[1, 0] == approx(
(0 * 5 + 7 * 4 + 2 * 3) / (3 * 5 + 7 * 4 + 2 * 3))
assert rewards[1, 1] == approx(
(3 * 3 + 0 * 4 + 0 * 5) / (3 * 3 + 7 * 4 + 2 * 5))
assert rewards[0].tolist() == [0.0, 0.0, 0]
assert rewards[1, 2] == approx(ratio)
assert rewards[2].tolist() == [0.0, 0.0, 0]
def test_single_game_term_exceeds_pool():
t = 1
batch_size = 1
torch.manual_seed(123)
np.random.seed(123)
s = ecn.State(**sampling.generate_batch(batch_size))
s.pool = torch.LongTensor([[3, 7, 2]])
s.utilities = torch.LongTensor([[[5, 4, 3], [3, 4, 5]]])
# last proposal means agent 0's, and we are now on agent 1, who is accepintg it
s.last_proposal = torch.LongTensor([[0, 2, 3]])
agent = 0 if t % 2 == 0 else 1
term = torch.ByteTensor([1])
rewards = rewards_lib.calc_rewards(s=s, t=t, term=term)
assert rewards[0].tolist() == [0, 0, 0]
def test_single_game_term_ideal():
t = 1
batch_size = 1
torch.manual_seed(123)
np.random.seed(123)
s = ecn.State(**sampling.generate_batch(batch_size))
s.pool = torch.LongTensor([[3, 7, 2]])
s.utilities = torch.LongTensor([[[5, 4, 3], [3, 4, 5]]])
# last proposal means agent 0's, and we are now on agent 1, who is accepintg it
s.last_proposal = torch.LongTensor([[3, 7, 0]])
agent = 0 if t % 2 == 0 else 1
term = torch.ByteTensor([1])
rewards = rewards_lib.calc_rewards(s=s, t=t, term=term)
assert rewards[0, 0] == approx((3 * 5 + 7 * 4) / (3 * 5 + 7 * 4 + 2 * 3))
assert rewards[0, 1] == approx((2 * 5) / (3 * 3 + 7 * 4 + 2 * 5))
assert rewards[0, 2] == 1.0
def test_single_game_term_t2_batch3_zero_term():
t = 2
batch_size = 3
torch.manual_seed(123)
np.random.seed(123)
s = ecn.State(**sampling.generate_batch(batch_size))
s.pool = torch.from_numpy(np.random.choice(10, (batch_size, 3))).long()
s.pool[1] = torch.LongTensor([3, 7, 2])
s.utilities = torch.from_numpy(np.random.choice(
10, (batch_size, 2, 3))).long()
s.utilities[1] = torch.LongTensor([[5, 4, 3], [3, 4, 5]])
s.last_proposal = torch.from_numpy(np.random.choice(
10, (batch_size, 3))).long()
s.last_proposal[1] = torch.LongTensor([3, 0, 0])
term = torch.ByteTensor([0, 0, 0])
s.pool[0] = s.last_proposal[0]
s.pool[2] = s.last_proposal[2]
total_available = 3 * 5 + 7 * 4 + 2 * 5
print('total_available', total_available)
# so, the proposer is the second agent, ie agent 1
# so, the proposer, agent 1, will take: 3 0 0
# accepter, agent 0, will take 0 7 2
actual = 0 * 5 + 7 * 4 + 2 * 3 + \
3 * 3
print('actual', actual)
ratio = actual / total_available
print('ratio', ratio)
agent = 0 if t % 2 == 0 else 1
rewards = rewards_lib.calc_rewards(s=s, t=t, term=term)
assert rewards[0].tolist() == [0.0, 0.0, 0]
assert rewards[1].tolist() == [0.0, 0.0, 0]
assert rewards[2].tolist() == [0.0, 0.0, 0]
