Rodrigo de Lazcano 2 年之前
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5142806039
共有 3 个文件被更改,包括 4 次插入5 次删除
  1. 2 2
      README.md
  2. 1 2
      gym_minigrid/manual_control.py
  3. 1 1
      setup.py

+ 2 - 2
README.md

@@ -138,10 +138,10 @@ use the `RGBImgPartialObsWrapper`. You can use it as follows:
 import gym
 from gym_minigrid.wrappers import RGBImgPartialObsWrapper, ImgObsWrapper
 
-env = gym.make('MiniGrid-Empty-8x8-v0', new_step_api=True)
+env = gym.make('MiniGrid-Empty-8x8-v0')
 env = RGBImgPartialObsWrapper(env) # Get pixel observations
 env = ImgObsWrapper(env) # Get rid of the 'mission' field
-obs = env.reset() # This now produces an RGB tensor only
+obs, _ = env.reset() # This now produces an RGB tensor only
 ```
 
 ## Design

+ 1 - 2
gym_minigrid/manual_control.py

@@ -11,7 +11,7 @@ def redraw(window, img):
 
 
 def reset(env, window, seed=None):
-    _ = env.reset(seed=seed)
+    env.reset(seed=seed)
 
     if hasattr(env, "mission"):
         print("Mission: %s" % env.mission)
@@ -101,7 +101,6 @@ if __name__ == "__main__":
 
     env = gym.make(
         args.env,
-        new_step_api=True,
         tile_size=args.tile_size,
     )
 

+ 1 - 1
setup.py

@@ -26,7 +26,7 @@ setup(
         "Programming Language :: Python :: 3.9",
         "Programming Language :: Python :: 3.10",
     ],
-    version="1.2.0",
+    version="1.2.1",
     keywords="memory, environment, agent, rl, gym",
     url="https://github.com/Farama-Foundation/gym-minigrid",
     description="Minimalistic gridworld reinforcement learning environments",