diff --git a/examples/shields/rl/checkpoint.py b/examples/shields/rl/checkpoint.py
new file mode 100644
index 0000000..65ca139
--- /dev/null
+++ b/examples/shields/rl/checkpoint.py
@@ -0,0 +1,104 @@
+
+
+import gymnasium as gym
+import minigrid
+
+from ray.tune import register_env
+from ray.rllib.algorithms.ppo import PPOConfig
+from ray.rllib.algorithms.dqn.dqn import DQNConfig
+from ray.tune.logger import pretty_print
+from ray.rllib.models import ModelCatalog
+
+from ray.rllib.algorithms.algorithm import Algorithm
+
+from torch_action_mask_model import TorchActionMaskModel
+from wrappers import OneHotShieldingWrapper, MiniGridShieldingWrapper
+from helpers import parse_arguments, create_log_dir, ShieldingConfig
+from shieldhandlers import MiniGridShieldHandler, create_shield_query
+from callbacks import MyCallbacks
+
+from ray.tune.logger import TBXLogger   
+import imageio
+
+import matplotlib.pyplot as plt
+
+
+def shielding_env_creater(config):
+    name = config.get("name", "MiniGrid-LavaSlipperyS12-v2")
+    framestack = config.get("framestack", 4)
+    args = config.get("args", None)
+    args.grid_path = F"{args.grid_path}_{config.worker_index}.txt"
+    args.prism_path = F"{args.prism_path}_{config.worker_index}.prism"
+    
+    shield_creator = MiniGridShieldHandler(args.grid_path, args.grid_to_prism_binary_path, args.prism_path, args.formula)
+    
+    env = gym.make(name)
+    env = MiniGridShieldingWrapper(env, shield_creator=shield_creator, shield_query_creator=create_shield_query)
+    # env = minigrid.wrappers.ImgObsWrapper(env)
+    # env = ImgObsWrapper(env)
+    env = OneHotShieldingWrapper(env,
+                        config.vector_index if hasattr(config, "vector_index") else 0,
+                        framestack=framestack
+                        )
+    
+    
+    return env
+
+
+def register_minigrid_shielding_env(args):
+    env_name = "mini-grid-shielding"
+    register_env(env_name, shielding_env_creater)
+
+    ModelCatalog.register_custom_model(
+        "shielding_model", 
+        TorchActionMaskModel
+    )
+    
+import argparse
+args = parse_arguments(argparse)
+    
+register_minigrid_shielding_env(args)
+    
+# Use the Algorithm's `from_checkpoint` utility to get a new algo instance
+# that has the exact same state as the old one, from which the checkpoint was
+# created in the first place:
+path_to_checkpoint = '/home/tknoll/Documents/Projects/log_results/PPO-shielding:full-evaluations:10-steps:20000-env:MiniGrid-LavaSlipperyS12-v2/PPO/PPO_mini-grid-shielding_8cd74_00000_0_2023-09-13_14-10-38/checkpoint_000005'
+
+
+algo = Algorithm.from_checkpoint(path_to_checkpoint)
+
+# Continue training.
+name = "MiniGrid-LavaSlipperyS12-v2"
+shield_creator = MiniGridShieldHandler(F"./{args.grid_path}_1.txt", args.grid_to_prism_binary_path, F"./{args.prism_path}_1.prism", args.formula)
+
+env = gym.make(name)
+env = MiniGridShieldingWrapper(env, shield_creator=shield_creator, shield_query_creator=create_shield_query)
+# env = minigrid.wrappers.ImgObsWrapper(env)
+# env = ImgObsWrapper(env)
+env = OneHotShieldingWrapper(env,
+                    0,
+                    framestack=4
+                    )
+
+episode_reward = 0
+terminated = truncated = False
+
+obs, info = env.reset()
+i = 0
+filenames = []
+while not terminated and not truncated:
+    action = algo.compute_single_action(obs)
+    obs, reward, terminated, truncated, info = env.step(action)
+    episode_reward += reward
+    filename = F"./frames/{i}.jpg"
+    img = env.get_frame()
+    plt.imsave(filename, img)
+    filenames.append(filename)
+    i  = i + 1
+    
+import imageio
+images = []
+for filename in filenames:
+    images.append(imageio.imread(filename))
+imageio.mimsave('./movie.gif', images)
+    
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diff --git a/examples/shields/rl/sandbox.py b/examples/shields/rl/sandbox.py
new file mode 100644
index 0000000..cc77202
--- /dev/null
+++ b/examples/shields/rl/sandbox.py
@@ -0,0 +1,66 @@
+import minigrid
+import gymnasium as gym
+import random
+import matplotlib.pyplot as plt
+
+from minigrid.wrappers import ImgObsWrapper, RGBImgPartialObsWrapper
+
+def main():
+    #samples = random.choices([0.0, 1.0], weights=(0.25, 0.75), k=100_000_000)
+    # print(samples)
+
+    # print(sum(samples))
+    #print(sum(samples) / len(samples))
+    
+    
+    
+    names = [
+              "MiniGrid-Adv-8x8-v0",
+              "MiniGrid-AdvSimple-8x8-v0",
+              "MiniGrid-AdvSlippery-8x8-v0",
+              "MiniGrid-AdvLava-8x8-v0",
+            # "MiniGrid-SingleDoor-7x6-v0",
+            # "MiniGrid-DoubleDoor-10x8-v0",
+            # "MiniGrid-DoubleDoor-12x12-v0",
+            # "MiniGrid-DoubleDoor-16x16-v0",
+            # "MiniGrid-LavaSlipperyS12-v0",
+            # "MiniGrid-LavaSlipperyS12-v1",
+            # "MiniGrid-LavaSlipperyS12-v2",
+            # "MiniGrid-LavaSlipperyS12-v3",
+            # "MiniGrid-LavaCrossingS9N3-v0",
+            # "MiniGrid-SimpleCrossingS9N3-v0",
+             
+            # "MiniGrid-ObstructedMaze-1Dlh-v0",
+            # "MiniGrid-BlockedUnlockPickup-v0",
+            # "MiniGrid-KeyCorridorS6R3-v0", 
+            # "MiniGrid-LockedRoom-v0",
+            # "MiniGrid-KeyCorridorS3R1-v0",
+            # "MiniGrid-LavaGapS7-v0",
+            # "MiniGrid-DoorKey-8x8-v0", 
+            # "MiniGrid-Dynamic-Obstacles-8x8-v0",
+            # "MiniGrid-Empty-Random-6x6-v0",
+            # "MiniGrid-Fetch-6x6-N2-v0", 
+            # "MiniGrid-FourRooms-v0", 
+            # "MiniGrid-LavaGapS7-v0",
+            # "MiniGrid-RedBlueDoors-6x6-v0",
+            ]
+    
+    for name in names:
+        env = gym.make(name) 
+        env = RGBImgPartialObsWrapper(env)
+        env = ImgObsWrapper(env)
+        
+        env.reset()
+        
+        img = env.get_frame(highlight=False)
+        plt.title(name)
+        plt.imshow(img)
+        f = open(F"{name}.txt", "w")
+        f.write(env.printGrid(init=True))
+        f.close()
+
+
+        plt.show()
+
+if __name__ == '__main__':
+    main()
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