4. Install Python 3.11 here .When installing check the box “add Python path” to add python to the environment variables of your computer.
5. Installing Python via this link automatically installs pip. To verify if pip is installed type “pip” in the terminal and it will respond with possible commands to use.
6. Install the dependencies given in the requirements.txt file by opening a terminal in the root directory of MAM-STM and use the command below to install the packages according to the configuration file requirements.txt.
```
pip install -r requirements.txt
```
...
...
@@ -24,11 +25,13 @@
purposes.
4. In the simulation mode, start and (sub)goal positions are set by default. They can be changed in the setupEnvironment function of env.py.
5. Various options are available to execute MAM-STM. These options are shown by typing the following command (see MAM-STM arguments)
```
python main.py -h
```
To run MAM-STM in simulation mode learning by learning to perform vertical manipulations using a self-designed environment “BR3”, and exploration rate of 30% type the following command.
```
python main.py -o Sim -a VERT -s BR3 -e 0.3
```
...
...
@@ -44,12 +47,18 @@
2. Set variables for the individual moieties MAM-STM should learn to control in the input.json file. This requires preemptive measurements to determine the moieties size (width, length, height) that need to be set in the input.json file. This enables the algorithm to determine the position and orientation of the moiety. Detailed descriptions of the necessary parameters are provided in the input.json file.
3. Various options are available to execute MAM-STM. These options are shown by typing the following command (see MAM-STM arguments)
```
python main.py -h
```
To run MAM-STM in experiment mode learning to perform vertical manipulations and use an exploration rate of 30% type the following command.
```
python main.py -o Exp -a VERT -e 0.3
```
The python is now connected to the STM. To determine the starting position of the moiety, a manual vertical manipulation approximately at the position of the moiety (using the imaging conditions) and read this positional information by utilizing the GUI pressing the “read position button”. This opens a prompt to determine the type of moiety that is maneuvered through the trajectory you are defining. The has to be equivalent to the moiety name defined in the input.json file: “species” “name”.
The python is now connected to the STM. To determine the starting position of the moiety, a manual vertical manipulation approximately at the position of the moiety (using the imaging conditions) and read this positional information by utilizing the GUI pressing the “read position button”. This opens a prompt to determine the type of moiety that is maneuvered through the trajectory you are defining. The has to be equivalent to the moiety name defined in the input.json file: “species” -> “name”.
4. Determine at least the final goal position or add additional sub goal positions by performing vertical manipulations (using imaging conditions) and read this positional information using the GUI. This creates an array of goal
positions the moiety will sequentially reach.
5. This runs the simulation either indefinitely or until the final goal is last defined goal is reached. To stop after the final goal is reached set the following variable under general_settings in the input.json file.
