论文标题
PDDLGYM：PDDL问题的健身环境
PDDLGym: Gym Environments from PDDL Problems
论文作者
论文摘要
我们提出PDDLGYM，该框架自动从PDDL域和问题构建OpenAI健身环境。 PDDLGYM中的观察和行动是关系的，使该框架特别适合于关系强化学习和关系顺序决策。 PDDLGYM也是一种通用框架，可快速从简洁而熟悉的规范语言中迅速构建众多不同的基准。我们讨论了设计决策和实施细节，并在计划和模型学习难度方面说明了20种内置环境之间的经验变化。我们希望PDDLGYM能够促进增强学习界（体育馆出现）与AI计划社区（生产PDDL）之间的桥梁建设。我们期待收集所有感兴趣的人的反馈，并相应地扩展一系列可用环境和功能。代码：https：//github.com/tomsilver/pddlgym
We present PDDLGym, a framework that automatically constructs OpenAI Gym environments from PDDL domains and problems. Observations and actions in PDDLGym are relational, making the framework particularly well-suited for research in relational reinforcement learning and relational sequential decision-making. PDDLGym is also useful as a generic framework for rapidly building numerous, diverse benchmarks from a concise and familiar specification language. We discuss design decisions and implementation details, and also illustrate empirical variations between the 20 built-in environments in terms of planning and model-learning difficulty. We hope that PDDLGym will facilitate bridge-building between the reinforcement learning community (from which Gym emerged) and the AI planning community (which produced PDDL). We look forward to gathering feedback from all those interested and expanding the set of available environments and features accordingly. Code: https://github.com/tomsilver/pddlgym
