import tensorflow as tf

class config():
  

    # env specific

    evaluate = False

    # output config
    output_path = "../results/policy_gradient/"
    model_output = output_path + "model.weights/"
    log_path = output_path + "log.txt"
    plot_output = output_path + "Policy_gradient.png"
    summary_freq = 1
    save_freq = 5

    
    # model and training config
    mode = "MC" # value can be "TD" or "MC"
    num_batches = 100 # number of batches trained on
    batch_size = 100 # number of steps used to compute each policy update, default is 1000
    max_ep_len = 100 # maximum episode length
    eval_batch_size = batch_size
    rand_begin = 0.2
    rand_end = 0
    rand_steps = num_batches
    learning_rate = 3e-2
    gamma = 0.98 # the discount factor
    use_baseline = True 
    normalize_advantage = True
    history_mul = 1
    restore = False

    # since we start new episodes for each batch
    assert max_ep_len <= batch_size
    assert rand_steps <= num_batches

a6ab0392aad5709344249a58c8fb7114bd548332
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class config:
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    <mask token>
    assert max_ep_len <= batch_size
    assert rand_steps <= num_batches

<mask token>


class config:
    evaluate = False
    output_path = '../results/policy_gradient/'
    model_output = output_path + 'model.weights/'
    log_path = output_path + 'log.txt'
    plot_output = output_path + 'Policy_gradient.png'
    summary_freq = 1
    save_freq = 5
    mode = 'MC'
    num_batches = 100
    batch_size = 100
    max_ep_len = 100
    eval_batch_size = batch_size
    rand_begin = 0.2
    rand_end = 0
    rand_steps = num_batches
    learning_rate = 0.03
    gamma = 0.98
    use_baseline = True
    normalize_advantage = True
    history_mul = 1
    restore = False
    assert max_ep_len <= batch_size
    assert rand_steps <= num_batches

import tensorflow as tf


class config:
    evaluate = False
    output_path = '../results/policy_gradient/'
    model_output = output_path + 'model.weights/'
    log_path = output_path + 'log.txt'
    plot_output = output_path + 'Policy_gradient.png'
    summary_freq = 1
    save_freq = 5
    mode = 'MC'
    num_batches = 100
    batch_size = 100
    max_ep_len = 100
    eval_batch_size = batch_size
    rand_begin = 0.2
    rand_end = 0
    rand_steps = num_batches
    learning_rate = 0.03
    gamma = 0.98
    use_baseline = True
    normalize_advantage = True
    history_mul = 1
    restore = False
    assert max_ep_len <= batch_size
    assert rand_steps <= num_batches

import tensorflow as tf

class config():
  

    # env specific

    evaluate = False

    # output config
    output_path = "../results/policy_gradient/"
    model_output = output_path + "model.weights/"
    log_path = output_path + "log.txt"
    plot_output = output_path + "Policy_gradient.png"
    summary_freq = 1
    save_freq = 5

    
    # model and training config
    mode = "MC" # value can be "TD" or "MC"
    num_batches = 100 # number of batches trained on
    batch_size = 100 # number of steps used to compute each policy update, default is 1000
    max_ep_len = 100 # maximum episode length
    eval_batch_size = batch_size
    rand_begin = 0.2
    rand_end = 0
    rand_steps = num_batches
    learning_rate = 3e-2
    gamma = 0.98 # the discount factor
    use_baseline = True 
    normalize_advantage = True
    history_mul = 1
    restore = False

    # since we start new episodes for each batch
    assert max_ep_len <= batch_size
    assert rand_steps <= num_batches
