diff --git a/scripts/enjoy.py b/scripts/enjoy.py
index 064e9c7..13ce7e0 100644
--- a/scripts/enjoy.py
+++ b/scripts/enjoy.py
@@ -11,6 +11,7 @@
     NTupleNetworkTDPolicy,
     NTupleNetworkTDPolicySmall,
 )
+from gymnasium_2048.agents.ntuple.search import ExpectimaxSearch
 
 
 def parse_args() -> argparse.Namespace:
@@ -117,6 +118,7 @@ def enjoy() -> None:
         env = gym.make(args.env, render_mode="human")
 
     policy = make_policy(algo=args.algo, trained_agent=args.trained_agent)
+    policy = ExpectimaxSearch(policy=policy)
 
     for _ in trange(args.n_episodes, desc="Enjoy"):
         play_game(env=env, policy=policy)
diff --git a/scripts/evaluate.py b/scripts/evaluate.py
index 9b1654b..5f69940 100644
--- a/scripts/evaluate.py
+++ b/scripts/evaluate.py
@@ -11,6 +11,7 @@
     NTupleNetworkTDPolicy,
     NTupleNetworkTDPolicySmall,
 )
+from gymnasium_2048.agents.ntuple.search import ExpectimaxSearch
 
 plt.style.use("ggplot")
 
@@ -181,6 +182,7 @@ def evaluate() -> None:
     env = make_env(env_id=args.env)
     if args.algo is not None and args.trained_agent is not None:
         policy = make_policy(algo=args.algo, trained_agent=args.trained_agent)
+        policy = ExpectimaxSearch(policy=policy)
     else:
         policy = None
 
diff --git a/src/gymnasium_2048/agents/ntuple/search.py b/src/gymnasium_2048/agents/ntuple/search.py
new file mode 100644
index 0000000..05f21fd
--- /dev/null
+++ b/src/gymnasium_2048/agents/ntuple/search.py
@@ -0,0 +1,68 @@
+import numpy as np
+
+from gymnasium_2048.agents.ntuple.policy import NTupleNetworkBasePolicy
+from gymnasium_2048.envs import TwentyFortyEightEnv
+
+
+class ExpectimaxSearch:
+    def __init__(
+        self,
+        policy: NTupleNetworkBasePolicy,
+        max_depth: int = 3,
+    ) -> None:
+        self.policy = policy
+        self.max_depth = max_depth
+        self.min_value = 0.0
+
+    def _evaluate(self, state: np.ndarray) -> tuple[float, int]:
+        values = [
+            self.policy.evaluate(state=state, action=action) for action in range(4)
+        ]
+        max_action = np.argmax(values)
+        return max(self.min_value, values[max_action]), max_action
+
+    def _maximize(self, state: np.ndarray, depth: int) -> tuple[float, int]:
+        if depth >= self.max_depth:
+            return self._evaluate(state=state)
+
+        max_value = self.min_value
+        max_action = 0
+
+        for action in range(4):
+            after_state, _, is_legal = TwentyFortyEightEnv.apply_action(
+                board=state,
+                action=action,
+            )
+            if not is_legal:
+                continue
+
+            value = self._chance(after_state=after_state, depth=depth + 1)
+            if value > max_value:
+                max_value = value
+                max_action = action
+
+        return max_value, max_action
+
+    def _chance(self, after_state: np.ndarray, depth: int) -> float:
+        if depth >= self.max_depth:
+            return self._evaluate(state=after_state)[0]
+
+        values, weights = [], []
+
+        for row in range(after_state.shape[0]):
+            for col in range(after_state.shape[1]):
+                if after_state[row, col] != 0:
+                    continue
+
+                for value, prob in ((1, 0.9), (2, 0.1)):
+                    after_state[row, col] = value
+                    values.append(self._maximize(state=after_state, depth=depth + 1)[0])
+                    weights.append(prob)
+                    after_state[row, col] = 0
+
+        return np.average(values, weights=weights)
+
+    def predict(self, state: np.ndarray) -> int:
+        value, action = self._maximize(state=state, depth=0)
+        print(value, action)
+        return action