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Generalizing LTL Instructions via Future Dependent Options
Artificial Intelligence · Submitted 8 Dec 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim In experiments on three different domains, we evaluate the LTL generalization capability of the agent trained by the proposed method, showing its advantage over previous representative methods.
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The abstract, sentence by sentence
In many real-world applications of control system and robotics, linear temporal logic (LTL) is a widely-used task specification language which has a compositional grammar that naturally induces temporally extended behaviours across tasks, including conditionals and alternative realizations. An important problem in RL with LTL tasks is to learn task-conditioned policies which can zero-shot generalize to new LTL instructions not observed in the training. However, because symbolic observation is often lossy and LTL tasks can have long time horizon, previous works can suffer from issues such as training sampling inefficiency and infeasibility or sub-optimality of the found solutions. In order to tackle these issues, this paper proposes a novel multi-task RL algorithm with improved learning efficiency and optimality. To achieve the global optimality of task completion, we propose to learn options dependent on the future subgoals via a novel off-policy approach. In order to propagate the rewards of satisfying future subgoals back more efficiently, we propose to train a multi-step value function conditioned on the subgoal sequence which is updated with Monte Carlo estimates of multi-step discounted returns. In experiments on three different domains, we evaluate the LTL generalization capability of the agent trained by the proposed method, showing its advantage over previous representative methods.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | In many real-world applications of control system and robotics, linear temporal logic (LTL) is a widely-used task specification language which has a compositional grammar that naturally induces temporally extended behaviours across tasks, including conditionals and alternative realizations. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | An important problem in RL with LTL tasks is to learn task-conditioned policies which can zero-shot generalize to new LTL instructions not observed in the training. | background 0.85 | 0.05 | 0.03 | asserted 1.00 |
| s2 | However, because symbolic observation is often lossy and LTL tasks can have long time horizon, previous works can suffer from issues such as training sampling inefficiency and infeasibility or sub-optimality of the found solutions. | gap 0.70 | 0.04 | 0.02 | hedged possibility 0.62 |
| s3 | In order to tackle these issues, this paper proposes a novel multi-task RL algorithm with improved learning efficiency and optimality. | contribution 1.00 | 0.76 | 0.20 | asserted 0.99 |
| s4 | To achieve the global optimality of task completion, we propose to learn options dependent on the future subgoals via a novel off-policy approach. | method 0.75 | 0.83 | 0.14 | asserted 0.99 |
| s5 | In order to propagate the rewards of satisfying future subgoals back more efficiently, we propose to train a multi-step value function conditioned on the subgoal sequence which is updated with Monte Carlo estimates of multi-step discounted returns. | method 0.91 | 0.89 | 0.06 | asserted 0.97 |
| s6 | In experiments on three different domains, we evaluate the LTL generalization capability of the agent trained by the proposed method, showing its advantage over previous representative methods. | result 0.96 | 0.96 | 0.66 | asserted 1.00 |
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The paper
Original abstract
In many real-world applications of control system and robotics, linear temporal logic (LTL) is a widely-used task specification language which has a compositional grammar that naturally induces temporally extended behaviours across tasks, including conditionals and alternative realizations. An important problem in RL with LTL tasks is to learn task-conditioned policies which can zero-shot generalize to new LTL instructions not observed in the training. However, because symbolic observation is often lossy and LTL tasks can have long time horizon, previous works can suffer from issues such as training sampling inefficiency and infeasibility or sub-optimality of the found solutions. In order to tackle these issues, this paper proposes a novel multi-task RL algorithm with improved learning efficiency and optimality. To achieve the global optimality of task completion, we propose to learn options dependent on the future subgoals via a novel off-policy approach. In order to propagate the rewards of satisfying future subgoals back more efficiently, we propose to train a multi-step value function conditioned on the subgoal sequence which is updated with Monte Carlo estimates of multi-step discounted returns. In experiments on three different domains, we evaluate the LTL generalization capability of the agent trained by the proposed method, showing its advantage over previous representative methods.
Human review
How the literature uses it
1 arXiv paper cite this in 2 sentences
Papers citing it · 1 sentence without a confident label
Cites it as background 1 paper
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In the context of RL from logical specifications, most works on generalization have focused on zero-shot generalization to unseen specifications that can be constructed from the seen (training) specifications [ref] .
Inductive Generalization in Reinforcement Learning from Specifications · Introduction · 0.91
What it cites · 5 sentences without a confident label
Contests 2 papers
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Some works [ref] solve new LTL instructions by leveraging the learned reusable skills or options, but produce sub-optimal or even infeasible solutions, especially when the symbolic observation is lossy, i.e., one symbolic state can correspond to multiple environment states.
LTL-Transfer: Skill Transfer for Temporal Task Specification · Introduction · contest 0.87
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These issues are common in real-world problems but ignored by previous LTL-RL works [ref] .
Systematic Generalisation through Task Temporal Logic and Deep Reinforcement Learning · Methodology · contest 0.71
Uses the method or tool of 1 paper
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As such, the agents in Baseline-2 and 3 are trained by off-policy Q learning [ref] or SAC [ref] approach which use the same hyper-parameters as the proposed method.
Soft Actor-Critic Algorithms and Applications · Baselines · 0.89
Cites as background 2 papers
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Learning independent option policies or skills for achieving each subgoal has been a common approach towards generalization in a temporal task setting for long [ref] .
Systematic Generalisation through Task Temporal Logic and Deep Reinforcement Learning · Related Work · 0.87
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Reinforcement learning (RL) is a promising framework for developing truly general agents capable of acting autonomously in the real world, ranging from video games [ref] to robotics [ref] .
Safe Human-Interactive Control via Shielding · Introduction · 0.91
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