@@ -29,14 +29,24 @@ These are the minimum steps required to replicate the results for simple_interse
* Run `./scripts/install_dependencies.sh` to install python dependencies.
* Low-level policies:
* To train all low-level policies from scratch: `python3 low_level_policy_main.py --train`
* To train a single low-level, for example wait: `python3 low_level_policy_main.py --option=wait --train`
* To test these trained low-level policies: `python3 low_level_policy_main.py --test --saved_policy_in_root`
* To test one of these trained low-level policies, for example wait: `python3 low_level_policy_main.py --option=wait --test --saved_policy_in_root`
* You can choose to train and test all the maneuvers. But this may take some time and is not recommended.
* To train all low-level policies from scratch: `python3 low_level_policy_main.py --train`. This may take some time.
* To test all these trained low-level policies: `python3 low_level_policy_main.py --test --saved_policy_in_root`.
* Make sure the training is fully complete before running above test.
* It is easier to verify few of the maneuvers using below commands:
* To train a single low-level, for example wait: `python3 low_level_policy_main.py --option=wait --train`.
* To test one of these trained low-level policies, for example wait: `python3 low_level_policy_main.py --option=wait --test --saved_policy_in_root`
* Available maneuvers are: wait, changelane, stop, keeplane, follow
* These results are visually evaluated.
* Note: This training has a high variance due to the continuous action space, especially for stop and keeplane maneuvers. It may help to train for 0.2 million steps than the default 0.1 million by adding argument '--nb_steps=200000' while training.
* High-level policy:
* To train high-level policy from scratch using the given low-level policies: `python3 high_level_policy_main.py --train`
* To evaluate this trained high-level policy: `python3 high_level_policy_main.py --evaluate --saved_policy_in_root`
* To run MCTS using the high-level policy: `python3 mcts.py`
* To evaluate this trained high-level policy: `python3 high_level_policy_main.py --evaluate --saved_policy_in_root`.
* The success average and standard deviation corresponds to the result from high-level policy experiments.
* To run MCTS using the high-level policy:
* To obtain a probabilites tree and save it: `python3 mcts.py --train`
* To evaluate using this saved tree: `python3 mcts.py --evaluate --saved_policy_in_root`.
* The success average and standard deviation corresponds to the results from MCTS experiments.