diff --git a/README.md b/README.md
index d6848ef8558086d11373274e8cdaf3381237c811..6fd0d0b131ef69d74ccc492f6d5fc87cd40e433d 100644
--- a/README.md
+++ b/README.md
@@ -18,22 +18,22 @@ A Python installation is needed to run the scripts.
 Install the following Python packages:
 
 ```
-import gym
-from stable_baselines3 import A2C
-from gymnasium.envs.registration import register
-from tqdm import tqdm
-import matplotlib.pyplot as plt
-import wandb
-from wandb.integration.sb3 import WandbCallback
-from stable_baselines3.common.vec_env import VecVideoRecorder
-import dill
-import zipfile
-import torch
-import torch.nn as nn
-import torch.optim as optim
-from tqdm import tqdm 
-import numpy as np
-from torch.distributions import Categorical
+pip install gym
+pip install stable_baselines3 
+pip install tqdm
+pip install wandb
+pip install moviepy
+pip install huggingface-sb3
+pip install tensorboard
+pip install panda-gym
+pip install dill
+pip install zipfile
+
+```
+Then run the scrpits in your command prompt: 
+
+```
+python "path_to_your_python_script"
 ```
 
 
@@ -65,7 +65,27 @@ The policy loss follows a decreasing trend, which is coherent to the model learn
 
 ### Full workflow with panda-gym
 
-The full training-visualization-sharing workflow is applied to the PandaReachJointsDense environment. The python script used is: ```a2c_sb3_panda_reach.py```.It appears that the PandaReachJointsDense-v3 environment is not known and could not be used (NameNotFound: Environment PandaReachJointsDense doesn't exist.)
+The full training-visualization-sharing workflow is applied to the PandaReachJointsDense environment. The python script used is: ```a2c_sb3_panda_reach.py```. It appears that the PandaReachJointsDense-v3 environment is not known and could not be used, as mentionned in the following error: 
+```
+---------------------------------------------------------------------------
+NameNotFound                              Traceback (most recent call last)
+<ipython-input-5-e21f3cb1d225> in <cell line: 21>()
+     19 register(id=env_id, entry_point='gym.envs.classic_control:CartPoleEnv', max_episode_steps=500)
+     20 
+---> 21 env = gym.make(env_id)
+     22 
+     23 model = A2C("MlpPolicy", env, verbose=1, tensorboard_log=f"runs/{run.id}")
+
+2 frames
+/usr/local/lib/python3.10/dist-packages/gym/envs/registration.py in _check_name_exists(ns, name)
+    210     suggestion_msg = f"Did you mean: `{suggestion[0]}`?" if suggestion else ""
+    211 
+--> 212     raise error.NameNotFound(
+    213         f"Environment {name} doesn't exist{namespace_msg}. {suggestion_msg}"
+    214     )
+
+NameNotFound: Environment PandaPushJointsDense doesn't exist. 
+```
 
 ## Contribute