

def make_experiment(exp_id=1, path='./Results/Temp/{domain}/{agent}/{representation}/', boyan_N0=119, initial_learn_rate=0.06, discretization=34):
    opt = {
        
    }
    opt['path'] = path
    opt['exp_id'] = exp_id
    opt['max_steps'] = 30000
    opt['num_policy_checks'] = 20
    opt['checks_per_policy'] = 1
    domain = FiniteCartPoleBalanceOriginal(good_reward=0.0)
    opt['domain'] = domain
    representation = IncrementalTabular(domain, discretization=discretization)
    policy = eGreedy(representation, epsilon=0.1)
    opt['agent'] = Q_Learning(policy, representation, discount_factor=exp_id.discount_factor, lambda_=0.9, initial_learn_rate=initial_learn_rate, learn_rate_decay_mode='boyan', boyan_N0=boyan_N0)
    experiment = Experiment(**opt)
    return experiment

dataset/ETHPy150Open rlpy/rlpy/examples/cartpole_orig/tabular.py make_experiment/VarMisuse@121/198 `domain`->`exp_id`