Metadata-Version: 2.1
Name: householdenv
Version: 0.0.1
Summary: This is an environment for RL purposes
Home-page: https://github.com/mrcabo/Household-env.git
Author: Diego Cabo Golvano
Author-email: dcgdiego@gmail.com
License: UNKNOWN
Project-URL: Source Code, https://github.com/mrcabo/Household-env.git
Description: # Household-env
        
        This is a gym environment that represents a robot agent in a household environment for RL purposes.
        
        #### Table of Contents  
        [How to run it](#how-to-run-it)  
        [Observation space](#observation-space)  
        [Action space](#action-space)  
        [Tasks](#tasks)
        
        
        ## How to run it
        
        To run an example of the environment first install it and then run the dummy file.
        ```bash
        #!/bin/bash
        pip install -e Household-env 
        python3 dummy.py
        ```
        
        ## Observation space
        
        The robot has a vision grid of 7x7. The vision grid inputs will return values that represent the content of that cell. 
        
        The tasks encoding is binary instead of label encoding (0: task1, 1: task2...) because there is no ordering for the
         tasks, but the alg. might think there is.
         
         **Note**: We could do the same for the vision grid, but then the observation space would increase a lot (48*3=147 for
          for only 7 representable objects )
        
        Num   | Observation                |  Min   |  Max
        ------|----------------------------|--------|-------
        0     | x_coord_robot              |  0     |  19
        1     | y_coord_robot              |  0     |  19
        2     | task_encoding              |  0     |  1
        ...   | ...                        |        |  
        6     | task_encoding              |  0     |  1
        7     | 1st action                 |  0     |  8
        8     | 2nd action                 |  0     |  8
        9     | 3rd action                 |  0     |  8
        10    | 4th action                 |  0     |  8
        (vision not yet)
        11    | vision_grid                |  0     |  1
        ..    | ..                         |  0     |  1
        58    | vision_grid                |  0     |  1
        
        Objects will return the following values when within range of the 7x7 vision grid.
        
        Num   | Object
        ------|---------------
        0     | Nothing
        1     | wall
        2     | TV
        3     | couch
        4     | bed
        5     | fridge
        6     | dishwasher
        7     | person
        
        ## Action space
        
        Only one action can be taken at each time step. The Num of the action to be taken is passed as the argument to the
         `step` function.
        
        Num   | Action                     |  Min   |  Max
        ------|----------------------------|--------|-------
        0     | move_up                    |  0     |  1
        1     | move_down                  |  0     |  1
        2     | move_left                  |  0     |  1
        3     | move_right                 |  0     |  1
        4     | (A) extend_arm             |  0     |  1
        5     | (B) retract_arm            |  0     |  1
        6     | (C) grasp                  |  0     |  1
        7     | (D) drop                   |  0     |  1
        8     | (E) push                   |  0     |  1
        
        ## Tasks
        
        The tasks encoding is binary instead of label encoding (0: task1, 1: task2...) because there is no ordering for the
         tasks, but the alg. might think there is. We currently use 5 units so up to 31 tasks.
         
        Num   | Action                     |  Binary encoded
        ------|----------------------------|-----------------
        0     | No tasks                   |  00000
        1     | Turn on TV                 |  00001
        2     | Bring user a drink         |  00010
        3     | Make beds                  |  00011
        
Keywords: Reinforcement Learning environment
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
