Works with basic test script

.gitignore

@@ -0,0 +1,6 @@
+*.swp
+*~
+*.egg-info
+__pycache__/
+build/
+dist/

gym_mygame/__init__.py

@@ -2,6 +2,6 @@
 
 register(
     id='MyGame-v0',
-    entry_point='gym_game.envs:MyGame',
+    entry_point='gym_mygame.envs:MyGame',
     max_episode_steps=10000
 )

gym_mygame/envs/__init__.py

@@ -1 +1 @@
-from mygame import MyGame
+from gym_mygame.envs.mygame import MyGame

gym_mygame/envs/mygame.py

@@ -16,17 +16,28 @@ def __init__(self):
 
         gym.Env.__init__(self)
 
+        # Use [-1,+1] for observation space an action space
+        self.observation_space = gym.spaces.Box(-1, +1, shape=(1,), dtype=np.float32)
+        self.action_space = gym.spaces.Box(-1, +1, (1,), dtype=np.float32)
+
+        self.reset()
+
     def step(self, action):
 
-        return
+        state = [0]
+        reward = 0
+        done = False
+        info = {}
 
-    def render(self):
+        return state, reward, done, info
+
+    def render(self, mode='human'):
 
         return
 
     def reset(self):
 
-        return
+        return [0]
 
     def close(self):
 

setup.py

@@ -1,3 +1,4 @@
+#!/usr/bin/env python3
 '''
 Python distutils setup file for gym-mygame module.
 

test.py

@@ -0,0 +1,27 @@
+#!/usr/bin/env python3
+'''
+Python distutils setup file for gym-mygame module.
+
+Adapted from http://gym.openai.com/docs/
+
+Copyright (C) 2020 Simon D. Levy
+
+MIT License
+'''
+
+import gym
+
+env = gym.make('gym_mygame:MyGame-v0')
+
+for i_episode in range(20):
+    observation = env.reset()
+    for t in range(100):
+        env.render()
+        print(observation)
+        action = env.action_space.sample()
+        observation, reward, done, info = env.step(action)
+        if done:
+            print("Episode finished after {} timesteps".format(t+1))
+            break
+
+env.close()