Merge branch 'a3c-tictactoe' into a3c-tictacote-lambda1

    a3c = dc.rl.A3C(env, policy, entropy_weight=0.01, value_weight=value_weight)

    a3c.optimizer = dc.models.tensorgraph.TFWrapper(

        tf.train.AdamOptimizer, learning_rate=0.01)

    avg_rewards = []

    for j in range(10):

        a3c.fit(rollouts)

        rewards = []

        for i in range(games):

                action = a3c.select_action(env._state)

                reward = env.step(action)

            rewards.append(reward)

    return np.mean(rewards)

        avg_rewards.append(((j+1) * rollouts, np.mean(rewards)))

    return avg_rewards

def main():

    scores = {}

    value_weight = 0.05

    while value_weight <= 1.0:

        print(value_weight)

    value_weight = 0.70

    score = eval_tic_tac_toe(value_weight)

        scores[value_weight] = score

        with open('tictactoe_value_search_lambda1.json', 'w') as fout:

            fout.write(json.dumps(scores))

        value_weight += 0.05

    with open('tictactoe_converge_lambda1.json', 'w') as fout:

        fout.write(json.dumps(score))