diff --git a/HW11/Minigrid/minigrid/__init__.py b/HW11/Minigrid/minigrid/__init__.py
index 6288130..535c119 100644
--- a/HW11/Minigrid/minigrid/__init__.py
+++ b/HW11/Minigrid/minigrid/__init__.py
@@ -21,6 +21,10 @@ except Exception:  # nosec
 
 
 def register_minigrid_envs():
+    register(
+        id="MiniGrid-MCHW11Env-v0",
+        entry_point="minigrid.envs:MCHW11Env",
+    )
     # BlockedUnlockPickup
     # ----------------------------------------
 
diff --git a/HW11/Minigrid/minigrid/envs/__init__.py b/HW11/Minigrid/minigrid/envs/__init__.py
index 12b1992..4e316ea 100644
--- a/HW11/Minigrid/minigrid/envs/__init__.py
+++ b/HW11/Minigrid/minigrid/envs/__init__.py
@@ -35,3 +35,4 @@ from minigrid.envs.putnear import PutNearEnv
 from minigrid.envs.redbluedoors import RedBlueDoorEnv
 from minigrid.envs.unlock import UnlockEnv
 from minigrid.envs.unlockpickup import UnlockPickupEnv
+from minigrid.envs.mchw11 import MCHW11Env
diff --git a/HW11/Minigrid/minigrid/envs/mchw11.py b/HW11/Minigrid/minigrid/envs/mchw11.py
new file mode 100644
index 0000000..98330ec
--- /dev/null
+++ b/HW11/Minigrid/minigrid/envs/mchw11.py
@@ -0,0 +1,77 @@
+from __future__ import annotations
+import math
+
+from numpy.random import default_rng
+
+from minigrid.core.constants import COLOR_NAMES
+from minigrid.core.grid import Grid
+from minigrid.core.mission import MissionSpace
+from minigrid.envs.lavaslippery import LavaSlipperyEnv
+from minigrid.core.world_object import (
+    Ball,
+    Box,
+    Key,
+    Slippery,
+    Lava,
+    Goal,
+    Point
+ )
+
+from minigrid.minigrid_env import MiniGridEnv, is_slippery
+
+import numpy as np
+
+from loguru import logger
+
+class MCHW11Env(LavaSlipperyEnv):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+    def _gen_grid(self, width, height):
+        super()._gen_grid(width, height)
+
+        self.probability_intended = 0.91
+
+        slippery = Slippery(probability_intended=self.probability_intended)
+        for x in range(1, self.width - 1):
+            for y in range(1, self.height - 1):
+                self.grid.set(x,y, slippery)
+
+        self.disable_random_start()
+
+        self.put_obj(Lava(),4,3)
+        self.put_obj(Lava(),7,3)
+        self.put_obj(Lava(),1,9)
+
+
+
+        agent_dir = 1 # We do not consider envs where the robot can turn
+        self.probability_turn_intended = 0.0
+        self.place_agent(agent_pos=np.array((1,1)), agent_dir=agent_dir, spawn_on_slippery=True)
+        self.place_goal(np.array((width - 2, height - 2)))
+        if self.dense_rewards: self.run_bfs()
+
+    def place_agent(self, spawn_on_slippery=False, agent_pos=None, agent_dir=0):
+        max_tries = 10_000
+        num_tries = 0
+
+        if self.randomize_start == True:
+            while True:
+                num_tries += 1
+                if num_tries > max_tries:
+                    raise RecursionError("rejection sampling failed in place_agent")
+                x = np.random.randint(0, self.width)
+                y = np.random.randint(0, 3)
+
+                cell = self.grid.get(*(x,y))
+                if cell is None or (cell.can_overlap() and not isinstance(cell, Lava) and not isinstance(cell, Goal) and (spawn_on_slippery or not is_slippery(cell))):
+                    self.agent_pos = np.array((x, y))
+                    self.agent_dir = np.random.randint(0, 4)
+                    break
+        elif agent_dir is None:
+            #self.agent_pos = np.array((1, 1))
+            self.agent_dir = 0
+        else:
+            self.agent_pos = agent_pos
+            self.agent_dir = agent_dir
+