def play_matches(player1, player2, games):
game = board.GoBoard()
currentGameCount = 0
scores = {"player1": 0, "player2": 0}
switchTurn = {-1: 1, 1: -1}
main_memory = Memory()
while currentGameCount < games:
game = board.GoBoard()
rand = random.randint(0, 1) * 2 - 1
if rand == -1:
playerTurn = -1
players = {
1: {
"agent": player1,
"name": "Player 1"
},
-1: {
"agent": player2,
"name": "Player 2"
}
}
else:
playerTurn = 1
players = {
1: {
"agent": player2,
"name": "Player 2"
},
-1: {
"agent": player1,
"name": "Player1"
}
}
game_running = 0
turn = 0
while game_running == 0:
memory_1 = Memory()
memory_2 = Memory()
turn += 1
# Make an action
#action ,pi, mcts_values, nn_value = players[playerTurn]["agent"].mcts.act()
new_state, value, preds = players[playerTurn]["agent"].act()
print(playerTurn)
print(players[playerTurn])
print("Value: " + str(value))
print(new_state.state)
if playerTurn == -1:
memory_1.add_to_memory(new_state.state)
else:
memory_2.add_to_memory(new_state.state)
# Switch the current player
playerTurn = switchTurn[playerTurn]
# Update the new current players game state
players[playerTurn]["agent"].oppAct(new_state)
if new_state.state.is_game_over():
print("Game " + str(currentGameCount) + " is over!")
winner = new_state.state.get_winner()
print(winner + " Won!")
if winner == "Player 1":
memory_1.declare_win_or_loss(1)
memory_2.declare_win_or_loss(-1)
else:
memory_2.declare_win_or_loss(1)
memory_1.declare_win_or_loss(-1)
main_memory.join_memories(memory_1)
main_memory.join_memories(memory_2)
game_running = 1
currentGameCount += 1
env = gym.make("CartPole-v0")
env.seed(1)
n_actions = env.action_space.n
cartpole_model = create_cartpole_model()
memory = Memory()
learning_rate = 1e-3
optimizer = tf.train.AdamOptimizer(learning_rate)
smoothed_reward = util.LossHistory(smoothing_factor=0.9)
plotter = util.PeriodicPlotter(sec=5, xlabel='Iterations', ylabel='Rewards')
for i_episode in range(10000):
plotter.plot(smoothed_reward.get())
observation = env.reset()
while True:
action = choose_action(cartpole_model, observation)
next_obs, reward, done, info = env.step(action)
memory.add_to_memory(observation, action, reward)
if done:
total_reward = sum(memory.rewards)
smoothed_reward.append(total_reward)
train_step(cartpole_model,
optimizer,
observations=np.vstack(memory.observations),
actions=np.array(memory.actions),
discounted_rewards=discount_rewards(memory.rewards))
memory.clear()
break
observation = next_obs
save_video_of_model(cartpole_model, "CartPole-v0")
