diff --git a/replay.py b/replay.py
new file mode 100755
index 0000000..448799c
--- /dev/null
+++ b/replay.py
@@ -0,0 +1,70 @@
+#!/bin/python3
+import gym
+from gym.envs.registration import register
+import numpy as np
+import os
+import time
+import datetime
+
+from stable_baselines3 import SAC, PPO, A2C
+from stable_baselines3.common.evaluation import evaluate_policy
+from stable_baselines3.common.policies import ActorCriticCnnPolicy, ActorCriticPolicy, MultiInputActorCriticPolicy
+
+from sb3_trl.trl_pg import TRL_PG
+import columbus
+
+
+def main(load_path, n_eval_episodes=0):
+    load_path = load_path.replace('.zip','')
+    load_path = load_path.replace("'",'')
+    load_path = load_path.replace(' ','')
+    file_name = load_path.split('/')[-1]
+    # TODO: Ugly, Ugly, Ugly:
+    env_name = file_name.split('_')[0]
+    alg_name = file_name.split('_')[1]
+    alg_deriv = file_name.split('_')[2]
+    use_sde = file_name.find('sde')!=-1
+    print(env_name, alg_name, alg_deriv, use_sde)
+    env = gym.make(env_name)
+    if alg_name=='ppo':
+        model = PPO(
+            "MlpPolicy",
+            env,
+            use_sde=use_sde,
+        )
+    elif alg_name=='trl' and alg_deriv=='pg':
+        model = TRL_PG(
+            "MlpPolicy",
+            env,
+            use_sde=use_sde,
+        )
+    else:
+        raise Exception('Algorithm not implemented for replay')
+
+    print(model.get_parameters())
+
+    model.load(load_path, env=env)
+
+    if n_eval_episodes:
+        mean_reward, std_reward = evaluate_policy(
+            model, env, n_eval_episodes=n_eval_episodes, deterministic=False)
+        print('Reward: '+str(round(mean_reward, 3)) +
+              '±'+str(round(std_reward, 2)))
+
+    input('<ready?>')
+    obs = env.reset()
+    episode_reward = 0
+    while True:
+        time.sleep(1/30)
+        action, _ = model.predict(obs, deterministic=False)
+        obs, reward, done, info = env.step(action)
+        env.render()
+        episode_reward += reward
+        if done:
+            episode_reward = 0.0
+            obs = env.reset()
+    env.reset()
+
+
+if __name__ == '__main__':
+    main(input('[path to model> '))