update benchmark and hyper parameter tuning readme

benchmarking/README.md

@@ -7,7 +7,7 @@ Configuration files can be loaded either from the CLI or from the Python API. Th
 ## CLI
 
 ```bash
-python -m imitation.scripts.<train_script> <algo> with benchmarking/<config_name>.json
+python -m imitation.scripts.<train_script> <algo> with src/imitation/config/tuned_hps/<config_name>.json
 ```
 `train_script` can be either 1) `train_imitation` with `algo` as `bc` or `dagger` or 2) `train_adversarial`  with `algo` as `gail` or `airl`.
 
@@ -16,26 +16,27 @@ python -m imitation.scripts.<train_script> <algo> with benchmarking/<config_name
 ```python
 ...
 from imitation.scripts.<train_script> import <train_ex>
-<train_ex>.run(command_name="<algo>", named_configs=["benchmarking/<config_name>.json"])
+<train_ex>.run(command_name="<algo>", named_configs=["src/imitation/config/tuned_hps/<config_name>.json"])
 ```
 
 # Tuning Hyperparameters
 
-The hyperparameters of any algorithm in imitation can be tuned using the `tuning.py` script.
+The hyperparameters of any algorithm in imitation can be tuned using the `scripts/tuning.py`.
 The benchmarking hyperparameter configs were generated by tuning the hyperparameters using
-the search space defined in the `tuning_config.py` script. The tuning script proceeds in two
-phases: 1) The hyperparameters are tuned using the search space provided, and 2) the best
-hyperparameter config found in the first phase based on the maximum mean return is
-re-evaluated on a separate set of seeds, and the mean and standard deviation of these trials
-are reported.
+the search space defined in the `scripts/config/tuning.py`.
 
-To tune the hyperparameters of an algorithm using the default search space provided:
+The tuning script proceeds in two phases:
+1. Tune the hyperparameters using the search space provided.
+2. Re-evaluate the best hyperparameter config found in the first phase based on the maximum mean return on a separate set of seeds. Report the mean and standard deviation of these trials.
+
+To use it with the default search space:
 ```bash
-python tuning.py with {algo} 'parallel_run_config.base_named_configs=["{env}"]'
+python src/imitation/scripts/tuning.py with <algo> 'parallel_run_config.base_named_configs=["<env>"]'
 ```
 
-In this command, `{algo}` provides the default search space and settings to be used for
-the specific algorithm, which is defined in the `tuning_config.py` script and
-`'parallel_run_config.base_named_configs=["{env}"]'` sets the environment to tune the algorithm in.
-See the documentation of `tuning.py` and `parallel.py` scripts for many other arguments that can be
+In this command: 
+- `<algo>` provides the default search space and settings for the specific algorithm, which is defined in the `scripts/config/tuning.py`
+- `<env>` sets the environment to tune the algorithm in. They are defined in the algo-specifc `scripts/config/train_[adversarial/imitation/preference_comparisons/rl].py` files.
+
+See the documentation of `scripts/tuning.py` and `scripts/parallel.py` for many other arguments that can be
 provided through the command line to change the tuning behavior.