DeepXube

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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,paper).

For any issues, you can create a GitHub issue or contact Forest Agostinelli (foresta@cse.sc.edu).

Overview:

Outline:

• Installation
• Environment
• Training Heuristic Function
• Heuristic Search
• Answer Set Programming Specification
• Examples

Installation

pip install deepxube

See 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 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 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 for more details.

Specifying Goals with Answer Set Programming

Coming soon.

Examples

• Specifying Goals to Deep Neural Networks with Answer Set Programming (paper, slides, code).