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: #ifndef _PTHREAD_H
  #define _PTHREAD_H 1
  
  #ifndef _INC_WINDOWS
  #ifndef WIN32_LEAN_AND_MEAN
  #define WIN32_LEAN_AND_MEAN 1
  #endif
  #include <Windows.h>
  #endif
  
  typedef CRITICAL_SECTION pthread_mutex_t;
  
  #endif /* pthread.h */
  