# <img src="./misc/images/scrambledCube.png" width="50"> DeepXube <img src="./misc/images/solvedCube.png" width="50">
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DeepXube (pronounced "Deep Cube") aims to solve classical planning problems in an explainable manner using deep reinforcement learning,
heuristic search, and formal logic. The current project can:
1) Train a heuristic function to estimate the cost-to-go between state/goal pairs,
where a goal represents a set of states considered goal states. The representation of the goal can come
in any form: i.e. a state, a set of ground atoms in first-order logic, natural language, an image/sketch, etc.
2) Specify goals with answer set programming, a robust form of logic programming, in the case where goals are represented as a set of ground atoms in first-order logic.
DeepXube is a generalization of DeepCubeA ([code](https://github.com/forestagostinelli/DeepCubeA/),[paper](https://cse.sc.edu/~foresta/assets/files/SolvingTheRubiksCubeWithDeepReinforcementLearningAndSearch_Final.pdf)).
For any issues, you can create a GitHub issue or contact Forest Agostinelli (foresta@cse.sc.edu).
**Overview**:\
<img src="./misc/images/overview.png" width="500">
**Outline**:
- [Installation](#installation)
- [Environment](#environment-implementation)
- [Training Heuristic Function](#training-heuristic-function)
- [Heuristic Search](#heuristic-search)
- [Answer Set Programming Specification](#specifying-goals-with-answer-set-programming)
- [Examples](#examples)
## Installation
`pip install deepxube`
See [INSTALL.md](INSTALL.md) for more details
## Environment
The environment includes a state object that defines states, a goal object that defines goals (a set of states considered goal states),
and an environment object that generate start states, define state transitions, when a state a goal state, and the neural network that takes states as an input.
See [ENVIRONMENT.md](ENVIRONMENT.md) for more details
## Training Heuristic Function
Once an environment has been implemented, a heuristic function can be trained to map states and goals to heuristic
values (estimates of the cost-to-go from a given start state to a given goal).
See [TRAIN.md](TRAIN.md) for more details.
## Heuristic Search
Given a trained heuristic function, a start state, and a goal, heuristic search is used to find a path from the start state
to the goal.
See [HEURSEARCH.md](HEURSEARCH.md) for more details.
## Specifying Goals with Answer Set Programming
Coming soon.
## Examples
Raw data
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"description": "# <img src=\"./misc/images/scrambledCube.png\" width=\"50\"> DeepXube <img src=\"./misc/images/solvedCube.png\" width=\"50\">\n\n\n--------------------------------------------------------------------------------\n\nDeepXube (pronounced \"Deep Cube\") aims to solve classical planning problems in an explainable manner using deep reinforcement learning, \nheuristic search, and formal logic. The current project can:\n\n1) Train a heuristic function to estimate the cost-to-go between state/goal pairs, \nwhere a goal represents a set of states considered goal states. The representation of the goal can come \nin any form: i.e. a state, a set of ground atoms in first-order logic, natural language, an image/sketch, etc.\n2) Specify goals with answer set programming, a robust form of logic programming, in the case where goals are represented as a set of ground atoms in first-order logic.\n\nDeepXube is a generalization of DeepCubeA ([code](https://github.com/forestagostinelli/DeepCubeA/),[paper](https://cse.sc.edu/~foresta/assets/files/SolvingTheRubiksCubeWithDeepReinforcementLearningAndSearch_Final.pdf)).\n\nFor any issues, you can create a GitHub issue or contact Forest Agostinelli (foresta@cse.sc.edu).\n\n**Overview**:\\\n<img src=\"./misc/images/overview.png\" width=\"500\">\n\n**Outline**:\n\n- [Installation](#installation)\n- [Environment](#environment-implementation)\n- [Training Heuristic Function](#training-heuristic-function)\n- [Heuristic Search](#heuristic-search)\n- [Answer Set Programming Specification](#specifying-goals-with-answer-set-programming)\n- [Examples](#examples)\n\n\n\n## Installation\n\n`pip install deepxube`\n\nSee [INSTALL.md](INSTALL.md) for more details\n\n## Environment\nThe environment includes a state object that defines states, a goal object that defines goals (a set of states considered goal states),\nand an environment object that generate start states, define state transitions, when a state a goal state, and the neural network that takes states as an input.\n\nSee [ENVIRONMENT.md](ENVIRONMENT.md) for more details\n\n\n## Training Heuristic Function\nOnce an environment has been implemented, a heuristic function can be trained to map states and goals to heuristic \nvalues (estimates of the cost-to-go from a given start state to a given goal).\n\nSee [TRAIN.md](TRAIN.md) for more details.\n\n## Heuristic Search\nGiven a trained heuristic function, a start state, and a goal, heuristic search is used to find a path from the start state \nto the goal.\n\nSee [HEURSEARCH.md](HEURSEARCH.md) for more details.\n\n## Specifying Goals with Answer Set Programming\nComing soon.\n\n## Examples\n",
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