text: import dataclasses
  
  @dataclasses.dataclass
  class DRQConfig:
      env = "cartpole_swingup"
      # IMPORTANT: if action_repeat is used the effective number of env steps needs to be
      # multiplied by action_repeat in the result graphs.
      # This is a common practice for a fair comparison.
      # See the 2nd paragraph in Appendix C of SLAC: https://arxiv.org/pdf/1907.00953.pdf
      # See Dreamer TF2's implementation: https://github.com/danijar/dreamer/blob/02f0210f5991c7710826ca7881f19c64a012290c/dreamer.py#L340
      action_repeat = 4
      # train
      num_train_steps = 1
      num_train_iters = 1
      # num_seed_steps can't be zero
      # and niter in train must be bigger than num_seed_steps
      num_seed_steps = 1
      replay_buffer_capacity = 100000
      seed = 1
      # eval
      eval_frequency = 5000
      # observation
      image_size = 84
      image_pad = 4
      frame_stack = 3
      # global params
      lr = 1e-3
      # IMPORTANT: please use a batch size of 512 to reproduce the results in the paper. Hovewer, with a smaller batch size it still works well.
      batch_size = 128
      # Agent configurations
      discount = 0.99
      init_temperature = 0.1
      actor_update_frequency = 2
      critic_tau = 0.01
      critic_target_update_frequency = 2
      # Actor configurations
      hidden_dim = 1024
      hidden_depth = 2
      log_std_bounds = [-10, 2]
      # Encoder configurations
      feature_dim = 50
      # obs data, relative to __file__
      obs_path = "obs.pkl"
  