Update README v2

nadimkanazi · Nov 3, 2023 · 9b88948 · 9b88948
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 # CACTO: Continuous Actor-Critic algorithm with Trajectory Optimization
-
-- ***main*** implements CACTO with state = *[x,t]*. Inputs: test-n (default: 0), system-id (default:'-'), seed (default: None), recover-training-flag (default: False), nb-cpus (default: 15), and w-S (default: 0).
+**Files**:
+- ***main*** implements CACTO with state = *[x,t]*. Inputs: test-n, system-id, seed, recover-training-flag, nb-cpus, and w-S.
 - ***TO*** implements the TO problem of the selected *system* whose end effector has to reach a target state while avoiding an obstacle and ensuring low control effort. The TO problem is modelled in *CasADi* and solved with *ipopt*.
 - ***RL*** implements the acotr-critic RL problem of the selected *system* whose end effector has to reach a target state while avoiding an obstacle and ensuring low control. It creates the state trajectory and controls to initialize TO.
 - ***NeuralNetwork*** contains the functions to create the NN-models and to compute the quantities needed to update them.
@@ -12,4 +12,25 @@
 - ***system_conf*** configures the training for the selected *system*. 
 - ***urdf*** contains *system* URDF file (double integrator and manipulator). 
 
-*Systems*: single integrator (system-id: single_integrator), double integrator (system-id: double_integrator), car (system-id: 'car'), car_park (system-id: 'car_park'), and 3 DOF planar manipulator (system-id: manipulator)
+**Systems**: 
+single integrator (system-id: single_integrator), double integrator (system-id: double_integrator), car (system-id: 'car'), car_park (system-id: 'car_park'), and 3 DOF planar manipulator (system-id: manipulator)
+
+**Inputs**:
+| Argument Name           | Type   | Default | Choices                                                                                              | Help                                |
+|-------------------------|--------|---------|------------------------------------------------------------------------------------------------------|-------------------------------------|
+| `--test-n`              | int    | 0       |                                                                                                      | Test number                         |
+| `--seed`                | int    | 0       |                                                                                                      | Random and tf.random seed           |
+| `--system-id`           | str    | 'single_integrator' | single_integrator, double_integrator, car, car_park, manipulator, ur5 | System-id (single_integrator, double_integrator, car, car_park, manipulator, ur5) |
+| `--recover-training-flag` | bool | False | True, False | Flag to recover training |
+| `--nb-cpus` | int | 2 | | Number of TO problems solved in parallel |
+| `--w-S` | float | 0 | | Sobolev training - weight of the value related error |
+
+
+Example of usage: 
+
+```python3 main.py --system-id='single_integrator' --seed=0 --nb-cpus=15 --w-S=1e-2 --test-n=0```
+- The "single_integrator" system is selected;
+- All the seeds are set to 0;
+- 15 TO problems are solved in parallel (if enough resources are available);
+- The weight of the value-error is set to 1e-2 (the value-gradient-error is set to 1). Note that w-S=0 corresponds to the standard CACTO algorithm (without Sobolev-Learning);
+- The information about the test and the results are stored in the folder N_try_0.