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Goal-conditioned reinforcement learning (RL) is a powerful approach for learning general-purpose skills by reaching diverse goals. However, it has limitations when it comes to task-conditioned policies, where goals are specified by temporally extended instructions written in the Linear Temporal Logic (LTL) formal language. Existing approaches for finding LTL-satisfying policies rely on sampling a large set of LTL instructions during training to adapt to unseen tasks at inference time. However, these approaches do not guarantee generalization to out-of-distribution LTL objectives, which may have increased complexity. In this paper, we propose a novel approach to address this challenge. We show that simple goal-conditioned RL agents can be instructed to follow arbitrary LTL specifications without additional training over the LTL task space. Unlike existing approaches that focus on LTL specifications expressible as regular expressions, our technique is unrestricted and generalizes to ω-regular expressions. Experiment results demonstrate the effectiveness of our approach in adapting goal-conditioned RL agents to satisfy complex temporal logic task specifications zero-shot.
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Combining Neural Networks and Tree Search for Task and Motion Planning in Challenging Environments
Task and motion planning subject to linear temporal logic (LTL) specifications in complex, dynamic environments requires efficient exploration of many possible future worlds. model‐free reinforcement learning has proven successful in a number of challenging tasks, but shows poor performance on tasks that require long‐term planning. in this work, we integrate Monte Carlo tree search with hierarchical neural net policies trained on expressive LTL specifications. we use reinforcement learning to find deep neural networks representing both low‐level control policies and task‐level ``option policies'' that achieve high‐level goals. our combined architecture generates safe and responsive motion plans that respect theLTL constraints. we demonstrate our approach in a simulated autonomous driving setting, where a vehicle must drive down a road in traffic, avoid collisions, and navigate an intersection, all while obeying rules of the road.
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- Award ID(s):
- 1637949
- PAR ID:
- 10047515
- Date Published:
- Journal Name:
- IROS 17/RLDM 17:
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
