run_experiments.md

Run Experiments

In this document, we give the user some advice to execute its experiments.

Warning

Please read our configs documentation before continuing to read this document.

Now that you are familiar with hydra and the organization of the configs of this repository, we can introduce few constraints to launch experiments:

1. When you launch an experiment you must specify the experiment config of the agent you want to train: python sheeprl.py exp=.... The list of the available experiment configs can be retrieved with the following command: python sheeprl.py --help
2. Then you have to specify the hyper-parameters of your experiment: you can override the hyper-parameters by specifying them as cli arguments (e.g., exp=dreamer_v3 algo=dreamer_v3 env=dmc env.wrapper.domain_name=walker env.wrapper.task_name=walk env.action_repeat=2 ...) or you can write your custom experiment file (you must put it in the ./sheeprl/configs/exp folder) and call your script with the command python sheeprl.py exp=custom_experiment (the last option is recommended). There are some available examples, just check the exp folder.
3. You cannot mix the agent command with the configs of another algorithm, this might raise an error or create anomalous behaviors. So if you want to train the dreamer_v3 agent, be sure to select the correct algorithm configuration (in our case algo=dreamer_v3)
4. To change the optimizer of an algorithm through the CLI you must do the following: suppose that you want to run an experiment with Dreamer-V3 and want to change the world model optimizer from Adam (default in the sheeprl/configs/algo/dreamer_v3.yaml config) with SGD, then in the CLI you must type python sheeprl.py algo=dreamer_v3 ... [email protected]_model.optimizer=sgd, where [email protected]_model.optimizer=sgd means that the optimizer field of the world_model of the algo config choosen (the dreamer_v3.yaml one) will be equal to the config sgd.yaml found under the sheeprl/configs/optim folder