diff --git a/frozenlakeplus/README.md b/frozenlakeplus/README.md
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+# FrozenLakePlus: Dynamic Ice Tile Environment ❄️
+
+This is a custom extension of OpenAI Gym's FrozenLake environment, where the map's slipperiness dynamically changes every few steps.
+
+## Features
+
+- Switches between slippery and non-slippery
+- Simple to plug into SB3 or custom agents
+- Great for teaching non-stationary RL
+
+## How to Run
+
+python test_frozenlake_plus.py
diff --git a/frozenlakeplus/eval_agent.py b/frozenlakeplus/eval_agent.py
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+++ b/frozenlakeplus/eval_agent.py
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+from frozenlake_plus_env import FrozenLakePlus
+from stable_baselines3 import PPO
+import numpy as np
+
+env = FrozenLakePlus(dynamic_slippery=False)
+model = PPO.load("ppo_frozenlakeplus", env=env)
+
+n_episodes = 10
+for episode in range(n_episodes):
+    obs, info = env.reset()
+    done = False
+    total_reward = 0
+    while not done:
+        action, _ = model.predict(obs, deterministic=True)
+        # Convert action to int if it's a numpy array
+        if isinstance(action, np.ndarray):
+            action = int(action.item())
+        obs, reward, terminated, truncated, info = env.step(action)
+        total_reward += reward
+        done = terminated or truncated
+    print(f"Episode {episode+1}: Total Reward = {total_reward}")
+
+env.close()
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diff --git a/frozenlakeplus/frozenlake_plus_env.py b/frozenlakeplus/frozenlake_plus_env.py
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+import gymnasium as gym
+from gymnasium.envs.toy_text.frozen_lake import FrozenLakeEnv
+import numpy as np
+
+class FrozenLakePlus(FrozenLakeEnv):
+    def __init__(self, map_name="4x4", is_slippery=True, dynamic_slippery=False, slippery_change_freq=10):
+        super().__init__(map_name=map_name, is_slippery=is_slippery)
+        self.dynamic_slippery = dynamic_slippery
+        self.slippery_change_freq = slippery_change_freq
+        self.step_count = 0
+        self.slippery = is_slippery
+        
+    def step(self, action):
+        self.step_count += 1
+        
+        if self.dynamic_slippery and self.step_count % self.slippery_change_freq == 0:
+            self.slippery = not self.slippery
+            self.is_slippery = self.slippery
+        
+        obs, reward, terminated, truncated, info = super().step(action)
+        
+        # Convert the observation to a numpy array
+        obs = np.array(obs)
+        
+        return obs, reward, terminated, truncated, info
+    
+    def reset(self, seed=None, options=None):
+        self.step_count = 0
+        return super().reset(seed=seed, options=options)
+
+__all__ = ["FrozenLakePlus"]
diff --git a/frozenlakeplus/ppo_frozenlakeplus.zip b/frozenlakeplus/ppo_frozenlakeplus.zip
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index 0000000000..25c6946673
Binary files /dev/null and b/frozenlakeplus/ppo_frozenlakeplus.zip differ
diff --git a/frozenlakeplus/test_frozenlake_plus.py b/frozenlakeplus/test_frozenlake_plus.py
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+++ b/frozenlakeplus/test_frozenlake_plus.py
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+from frozenlake_plus_env import FrozenLakePlus
+
+env = FrozenLakePlus(dynamic_slippery=True, slippery_change_freq=3)
+obs, info = env.reset()
+
+done = False
+step = 0  # Initialize step counter
+while not done:
+    obs, reward, terminated, truncated, info = env.step(env.action_space.sample())
+    step += 1  # Increment step counter
+    print(f"Step: {step}, Obs: {obs}, Reward: {reward}, Terminated: {terminated}, Truncated: {truncated}")
+    done = terminated or truncated
+env.close()
diff --git a/frozenlakeplus/train_agent.py b/frozenlakeplus/train_agent.py
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+++ b/frozenlakeplus/train_agent.py
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+from frozenlake_plus_env import FrozenLakePlus
+from stable_baselines3 import PPO
+from stable_baselines3.common.env_util import make_vec_env
+
+env = make_vec_env(lambda: FrozenLakePlus(dynamic_slippery=True), n_envs=1)
+
+print("Starting training...")
+model = PPO("MlpPolicy", env, verbose=2)
+model.learn(total_timesteps=10_000)
+print("Training finished. Model saved as ppo_frozenlakeplus.zip")
+
+model.save("ppo_frozenlakeplus")
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