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README.md

@@ -45,7 +45,7 @@ RL Zoo branch: `feat/gym-donkeycar`
 ```
 export AE_PATH=/path/to/match_ae-32_monaco_warren.pkl
 # Download model and save it into the logs/ folder
-python -m utils.load_from_hub --algo tqc --env donkey-warren-track-v0 -orga araffin -f logs/
+python -m rl_zoo3.load_from_hub --algo tqc --env donkey-warren-track-v0 -orga araffin -f logs/
 python enjoy.py --algo tqc --env donkey-warren-track-v0  -f logs/
 ```
 
@@ -53,7 +53,7 @@ python enjoy.py --algo tqc --env donkey-warren-track-v0  -f logs/
 ```
 python train.py --algo tqc --env donkey-warren-track-v0 -f logs/
 # Upload the model and generate video (when possible)
-python -m utils.push_to_hub --algo tqc --env donkey-warren-track-v0 -f logs/ -orga araffin
+python -m rl_zoo3.push_to_hub --algo tqc --env donkey-warren-track-v0 -f logs/ -orga araffin
 ```
 
 ## Hyperparameters
@@ -61,13 +61,13 @@ python -m utils.push_to_hub --algo tqc --env donkey-warren-track-v0 -f logs/ -or
 OrderedDict([('batch_size', 256),
              ('buffer_size', 200000),
              ('callback',
-              [{'utils.callbacks.ParallelTrainCallback': {'gradient_steps': 200}},
-               'utils.callbacks.LapTimeCallback']),
+              [{'rl_zoo3.callbacks.ParallelTrainCallback': {'gradient_steps': 200}},
+               'rl_zoo3.callbacks.LapTimeCallback']),
              ('ent_coef', 'auto'),
              ('env_wrapper',
               [{'gym.wrappers.time_limit.TimeLimit': {'max_episode_steps': 10000}},
                'ae.wrapper.AutoencoderWrapper',
-               {'utils.wrappers.HistoryWrapper': {'horizon': 2}}]),
+               {'rl_zoo3.wrappers.HistoryWrapper': {'horizon': 2}}]),
              ('gamma', 0.99),
              ('gradient_steps', 256),
              ('learning_rate', 0.00073),