Solving Hard AI Planning Instances Using Curriculum-Driven Deep Reinforcement Learning

@inproceedings{Feng2020SolvingHA,
  title={Solving Hard AI Planning Instances Using Curriculum-Driven Deep Reinforcement Learning},
  author={Dieqiao Feng and Carla P. Gomes and Bart Selman},
  booktitle={International Joint Conference on Artificial Intelligence},
  year={2020}
}
Despite significant progress in general AI planning, certain domains remain out of reach of current AI planning systems. Sokoban is a PSPACE-complete planning task and represents one of the hardest domains for current AI planners. Even domain-specific specialized search methods fail quickly due to the exponential search complexity on hard instances. Our approach based on deep reinforcement learning augmented with a curriculum-driven method is the first one to solve hard instances within one day… 

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References

SHOWING 1-10 OF 34 REFERENCES

Learning Generalized Reactive Policies using Deep Neural Networks

This work shows that a deep neural network can be used to learn and represent a generalized reactive policy (GRP) that maps a problem instance and a state to an action, and that the learned GRPs efficiently solve large classes of challenging problem instances.

Structure and inference in classical planning

It is shown that many of the standard benchmark domains can be solved with almost no search or a polynomially bounded amount of search, once the structure of planning problems is taken into account.

Goal-Based Action Priors

This work develops a framework for goal and state dependent action priors that can be used to prune away irrelevant actions based on the robot’s current goal, thereby greatly accelerating planning in a variety of complex stochastic environments.

Imagination-Augmented Agents for Deep Reinforcement Learning

Imagination-Augmented Agents (I2As), a novel architecture for deep reinforcement learning combining model-free and model-based aspects, shows improved data efficiency, performance, and robustness to model misspecification compared to several baselines.

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm

This paper generalises the approach into a single AlphaZero algorithm that can achieve, tabula rasa, superhuman performance in many challenging domains, and convincingly defeated a world-champion program in each case.

What good are actions? Accelerating learning using learned action priors

This work extends its method to base action priors on perceptual cues rather than absolute states, allowing the transfer of these priors between tasks with differing state spaces and transition functions, and demonstrates experimentally the advantages of learning withaction priors in a reinforcement learning context.

The first learning track of the international planning competition

The competition results show that at this stage no learning for planning system outperforms state-of-the-art planners in a domain independent manner across a wide range of domains, but systems appear to be close to providing such performance.

GRASP: A Search Algorithm for Propositional Satisfiability

Experimental results obtained from a large number of benchmarks indicate that application of the proposed conflict analysis techniques to SAT algorithms can be extremely effective for aLarge number of representative classes of SAT instances.

Playing Atari with Deep Reinforcement Learning

This work presents the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning, which outperforms all previous approaches on six of the games and surpasses a human expert on three of them.

PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains

The syntax of the language, PDDL2.1, is described, which has considerable modelling power -- exceeding the capabilities of current planning technology -- and presents a number of important challenges to the research community.