arXiv Artificial Intelligence

Reinforcement Learning with Temporal-Logic-Based Causal Diagrams

2 hours ago 11.09.2026 07:00 👁 2 views
Reinforcement Learning with Temporal-Logic-Based Causal Diagrams

Quick summary

arXiv:2306.13732v2 Announce Type: replace Abstract: We study a class of reinforcement learning (RL) tasks where the objective of the agent is to accomplish temporally extended goals. In this setting, a common approach is to represent the tasks as deterministic finite automata (DFA) and integrate them into the state-space for RL algorithms. However, while these machines model the reward function, they often overlook the causal knowledge about the environment. To address this limitation, we propose the Temporal-Logic-based Causal Diagram (TL-CD) in RL, which captures the temporal causal relation

Key takeaways

  • arXiv:2306.13732v2 Announce Type: replace Abstract: We study a class of reinforcement learning (RL) tasks where the objective of the agent is to accomplish temporally extended goals.
  • In this setting, a common approach is to represent the tasks as deterministic finite automata (DFA) and integrate them into the state-space for RL algorithms.
  • However, while these machines model the reward function, they often overlook the causal knowledge about the environment.

Why it matters

“Reinforcement Learning with Temporal-Logic-Based Causal Diagrams” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗