Update README.md

README.md

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 # SPO outperforms PPO in all environments when the network deepens (five random seeds):
 ![MuJoCo](https://github.com/MyRepositories-hub/Simple-Policy-Optimization/blob/main/draw_return_mujoco.png)
+
+# Training
+**The experimental environment is `gymnasium`, and you need to execute the following command to install the dependencies:**
+## MuJoCo
+### Installation
+```bash
+pip install gymnasium
+pip install gymnasium[mujoco]
+```
+### Reminder
+Please change the code from 
+```python
+self.add_overlay(bottomleft, "Solver iterations", str(self.data.solver_iter + 1))
+```
+to 
+```python
+self.add_overlay(bottomleft, "Solver iterations", str(self.data.solver_niter + 1))
+```
+in line 593 of the file path `venv\Lib\site-packages\gymnasium\envs\mujoco\mujoco_rendering.py` to resolve the error
+
+### Running
+```python
+import gymnasium as gym
+
+env = gym.make('Humanoid-v4', render_mode='human')
+while True:
+    s, _ = env.reset()
+    done = False
+    while not done:
+        a = env.action_space.sample()
+        s_next, r, dw, tr, info = env.step(a)
+        done = (dw or tr)
+```
+## Atari
+### Installation
+```bash
+pip install gymnasium[atari]
+pip install gymnasium[accept-rom-license]
+```
+### Running
+```python
+import gymnasium as gym
+
+env = gym.make('ALE/Breakout-v5', render_mode='human')
+while True:
+    s, _ = env.reset()
+    done = False
+    while not done:
+        a = env.action_space.sample()
+        s_next, r, dw, tr, info = env.step(a)
+        done = (dw or tr)
+```