hunk.code_window:     # Unsquash actions to the upper and lower bounds of env's action space.
       "normalize_actions": True,
       # === Learning ===
       # Update the target by \tau * policy + (1-\tau) * target_policy
       "tau": 5e-3,
       # Target entropy lower bound. This is the inverse of reward scale,
       # and will be optimized automatically.
       "target_entropy": "auto",
  
hunk.labels: keep keep keep keep replace keep keep keep keep
hunk.after_edit:     # Update the target by \tau * policy + (1-\tau) * target_policy.
  
hunk.file_path: rllib/agents/sac/sac.py
hunk.type: replace
file: pendulum-sac:
      env: Pendulum-v0
      run: SAC
      stop:
          episode_reward_mean: -300  # note that evaluation perf is higher
          timesteps_total: 10000
      config:
          evaluation_interval: 1  # logged under evaluation/* metric keys
          soft_horizon: True
          clip_actions: False
          normalize_actions: True
          metrics_smoothing_episodes: 5
  