Learning to maintain safety through expert demonstrations in settings with unknown constraints: A Q-learning perspective
arXiv:2602.23816v1 Announce Type: new Abstract: Given a set of trajectories demonstrating the execution of a task safely in a constrained MDP with observable rewards but with unknown constraints and non-observable costs, we aim to find a policy that maximizes the likelihood of demonstrated trajectories trading the balance between being conservative and increasing significantly the likelihood of high-rewarding trajectories but with potentially unsafe steps. Having these objectives, we aim towards learning a policy that maximizes the probability of the most $promising$ trajectories with respect to the demonstrations. In so doing, we formulate the ``promise" of individual state-action pairs in terms of $Q$ values, which depend on task-specific rewards as well as on the assessment of states' safety, mixing expectations in terms of rewards and safety. This entails a safe Q-learning perspective of the inverse learning problem under constraints: The devised Safe $Q$ Inverse Constrained Reinf
arXiv:2602.23816v1 Announce Type: new Abstract: Given a set of trajectories demonstrating the execution of a task safely in a constrained MDP with observable rewards but with unknown constraints and non-observable costs, we aim to find a policy that maximizes the likelihood of demonstrated trajectories trading the balance between being conservative and increasing significantly the likelihood of high-rewarding trajectories but with potentially unsafe steps. Having these objectives, we aim towards learning a policy that maximizes the probability of the most $promising$ trajectories with respect to the demonstrations. In so doing, we formulate the ``promise" of individual state-action pairs in terms of $Q$ values, which depend on task-specific rewards as well as on the assessment of states' safety, mixing expectations in terms of rewards and safety. This entails a safe Q-learning perspective of the inverse learning problem under constraints: The devised Safe $Q$ Inverse Constrained Reinforcement Learning (SafeQIL) algorithm is compared to state-of-the art inverse constraint reinforcement learning algorithms to a set of challenging benchmark tasks, showing its merits.
Executive Summary
This article proposes a novel Q-learning approach, Safe Q Inverse Constrained Reinforcement Learning (SafeQIL), to learn safe policies in environments with unknown constraints. The algorithm balances conservativeness and exploration to maximize the likelihood of high-rewarding trajectories while ensuring safety. The authors demonstrate the effectiveness of SafeQIL through benchmark tasks, showcasing its potential in constrained reinforcement learning.
Key Points
- ▸ SafeQIL algorithm formulation
- ▸ Balancing conservativeness and exploration
- ▸ Comparison to state-of-the-art inverse constraint reinforcement learning algorithms
Merits
Effective Safety Assessment
The SafeQIL algorithm assesses states' safety and mixes expectations in terms of rewards and safety, providing a comprehensive approach to learning safe policies.
Demerits
Limited Generalizability
The algorithm's performance may be limited to the specific task and environment in which it was trained, requiring further research to ensure generalizability.
Expert Commentary
The SafeQIL algorithm represents a significant contribution to the field of constrained reinforcement learning, as it effectively balances the trade-off between exploration and safety. The use of Q-values to assess the 'promise' of individual state-action pairs is a novel approach that holds promise for improving the safety and efficiency of reinforcement learning algorithms. However, further research is needed to address the limitations of the algorithm and ensure its generalizability to a wide range of tasks and environments.
Recommendations
- ✓ Further research on generalizability and transfer learning
- ✓ Exploration of applications in high-stakes decision-making domains