# Save the hands as training data for the betting NN
init_hands[p].sort()
x_train.append(init_hands[p].get_cards_as_matrix())
y_train.append(game.tricks[p])
# Save the data from the game
Hands.append(game.initialHands)
History.append(game.h)
Bets.append(game.bets)
Scores.append(scores)
Tricks.append(game.tricks)
# Save the game state as training data for the playing NN
for rd in range(n):
# Get the game state up until this round
round_state = game.action_state(rd)
# Save data for training
for p in range(4):
# Make the state relative to player p
state = game.relativize_state(round_state, p, rd)
for key in state:
x_train_RL[key].append(state[key])
# Target Q value
if rd == n: # Final round in this episode; reward is final total team score
y_hat = game.tricks[p] + game.tricks[(p + 2) % 4]
#y_hat = scores[p] + scores[(p+2)%4]
else: # More rounds left; get reward based on target action
y_hat = gamma * np.max(
target_action_model.predict(
History.append(game.h)
Bets.append(game.bets)
Scores.append(scores)
Tricks.append(game.tricks)
# Save data for training; pick a random player and train with their data
for p in range(4): #rnd.sample(range(4),1):
# Save the hands as training data for the betting NN
init_hands[p].sort()
x_train.append(init_hands[p].get_cards_as_matrix())
y_train.append(game.tricks[p])
# Save the game state as training data for the playing NN
for rd in range(n):
# Get and update the game state based on the action taken
state = game.action_state(p, rd)
for key in state:
x_train_RL[key].append(state[key])
# REWARD:
my_team_score = int(game.T[rd] == p or game.T[rd] == ((p + 2) % 4))
discounted_reward = gamma**(n - 1 - rd) * (scores[p] +
scores[(p + 2) % 4])
y_train_RL.append(discounted_reward + my_team_score)
# Train the betting NN
if t % train_interval == 0:
print 'Training betting...'
# Train the NN
hist = batch_loss_history()
bet_model.fit(np.asarray(x_train),
np.asarray(y_train),
