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CAT: Caution Aware Transfer in Reinforcement Learning via Distributional Risk

Mohamad Fares El Hajj Chehade, Amrit Singh Bedi, Amy Zhang, Hao Zhu

TL;DR

The paper addresses safe transfer in reinforcement learning by introducing Caution-Aware Transfer (CAT), a framework that treats risk as a general notion via state-action occupancy measures and optimizes a weighted return-caution objective during test-time transfer. It maintains risk-neutral source policies and constructs a test-time policy by selecting actions that maximize a combined score $Q^{\pi_j}_i(s,b) - c\rho_i(d^{\pi_j})$, where $\rho_i$ captures barrier, variance, or divergence-based caution. Theoretical analysis provides a suboptimality bound on theCAT policy relative to source policies, and an extension to CAT-SF enables efficient test-time evaluation with successor features. Empirically, CAT yields safer policies in Gridworld and Reacher, highlighting its practical impact for deploying RL agents under uncertain and varied risk conditions. These contributions advance data-efficient and safety-aware transfer in RL with broad applicability to real-world systems.

Abstract

Transfer learning in reinforcement learning (RL) has become a pivotal strategy for improving data efficiency in new, unseen tasks by utilizing knowledge from previously learned tasks. This approach is especially beneficial in real-world deployment scenarios where computational resources are constrained and agents must adapt rapidly to novel environments. However, current state-of-the-art methods often fall short in ensuring safety during the transfer process, particularly when unforeseen risks emerge in the deployment phase. In this work, we address these limitations by introducing a novel Caution-Aware Transfer Learning (CAT) framework. Unlike traditional approaches that limit risk considerations to mean-variance, we define "caution" as a more generalized and comprehensive notion of risk. Our core innovation lies in optimizing a weighted sum of reward return and caution-based on state-action occupancy measures-during the transfer process, allowing for a rich representation of diverse risk factors. To the best of our knowledge, this is the first work to explore the optimization of such a generalized risk notion within the context of transfer RL. Our contributions are threefold: (1) We propose a Caution-Aware Transfer (CAT) framework that evaluates source policies within the test environment and constructs a new policy that balances reward maximization and caution. (2) We derive theoretical sub-optimality bounds for our method, providing rigorous guarantees of its efficacy. (3) We empirically validate CAT, demonstrating that it consistently outperforms existing methods by delivering safer policies under varying risk conditions in the test tasks.

CAT: Caution Aware Transfer in Reinforcement Learning via Distributional Risk

TL;DR

The paper addresses safe transfer in reinforcement learning by introducing Caution-Aware Transfer (CAT), a framework that treats risk as a general notion via state-action occupancy measures and optimizes a weighted return-caution objective during test-time transfer. It maintains risk-neutral source policies and constructs a test-time policy by selecting actions that maximize a combined score , where captures barrier, variance, or divergence-based caution. Theoretical analysis provides a suboptimality bound on theCAT policy relative to source policies, and an extension to CAT-SF enables efficient test-time evaluation with successor features. Empirically, CAT yields safer policies in Gridworld and Reacher, highlighting its practical impact for deploying RL agents under uncertain and varied risk conditions. These contributions advance data-efficient and safety-aware transfer in RL with broad applicability to real-world systems.

Abstract

Transfer learning in reinforcement learning (RL) has become a pivotal strategy for improving data efficiency in new, unseen tasks by utilizing knowledge from previously learned tasks. This approach is especially beneficial in real-world deployment scenarios where computational resources are constrained and agents must adapt rapidly to novel environments. However, current state-of-the-art methods often fall short in ensuring safety during the transfer process, particularly when unforeseen risks emerge in the deployment phase. In this work, we address these limitations by introducing a novel Caution-Aware Transfer Learning (CAT) framework. Unlike traditional approaches that limit risk considerations to mean-variance, we define "caution" as a more generalized and comprehensive notion of risk. Our core innovation lies in optimizing a weighted sum of reward return and caution-based on state-action occupancy measures-during the transfer process, allowing for a rich representation of diverse risk factors. To the best of our knowledge, this is the first work to explore the optimization of such a generalized risk notion within the context of transfer RL. Our contributions are threefold: (1) We propose a Caution-Aware Transfer (CAT) framework that evaluates source policies within the test environment and constructs a new policy that balances reward maximization and caution. (2) We derive theoretical sub-optimality bounds for our method, providing rigorous guarantees of its efficacy. (3) We empirically validate CAT, demonstrating that it consistently outperforms existing methods by delivering safer policies under varying risk conditions in the test tasks.
Paper Structure (21 sections, 13 theorems, 38 equations, 7 figures, 1 table, 3 algorithms)

This paper contains 21 sections, 13 theorems, 38 equations, 7 figures, 1 table, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Works
  3. Preliminaries
  4. Markov Decision Process Modeling
  5. Problem Formulation: Risk-aware Transfer
  6. The Risk Aware Transfer Learning Problem
  7. Proposed Approach: CAT in RL
  8. Caution Aware Transfer
  9. Theoretical Results
  10. Cautious Transfer with Successor Features
  11. Experiments
  12. Gridworld
  13. Reacher
  14. Conclusions
  15. Appendix
  16. ...and 6 more sections

Key Result

Theorem 1

Let $M_i \in \mathcal{M}$ and let $Q_i^{\pi_j^*}$ be the action-value function of an optimal (caution-aware or caution-neutral) policy $\pi_j^*$ of $M_j \in \mathcal{M}$ when evaluated in $M_i$, and let $\rho_i(d^{\pi_j^*})$ be an $L$-Lipschitz caution factor of $\pi_j^*$ in $M_i$, bounded by a cons Then, it holds that

Figures (7)

  • Figure 1: Illustration of the transfer problem. A set of risk-neutral optimal policies are first evaluated in a new task $i$. Then, the policy $\pi_i$ for that task is designed on a state-by-state basis, by applying $f$ to the resulting action values.
  • Figure 2: (a), (b) show the risk-aware optimal policies for each of the training tasks. (c) shows the results of risk-aware and risk-neutral transfer. Risk-aware transfer fails to find a path to the goal, whereas risk-neutral transfer finds a risky path, both showing significant failure rates in the new task, as indicated in (d).
  • Figure 3: The set of source tasks are transferred to different test tasks, grouped by their objectives. The test tasks might wish to include a risk factor in their objective, which could be minimizing variance, aligning with expert behavior, or constraining the occupancy of states. Some tasks may not even include any type of risk, solely focusing on maximizing expected return.
  • Figure 4: The optimal risk-neutral policies for the three source tasks: $\pi_1^*$, $\pi_2^*$ and $\pi_3^*$. $\pi_2^*$ and $\pi_3^*$ have no knowledge of risk and cross the block.
  • Figure 5: The performance of CAT and the baseline on 10 different test tasks. For example, in (a) and (b), since the structure of the gray block drastically changes, the baseline method, which uses a mean-variance objective, fails to find a path to the goal. In (h), the structure of the risk remains the same as in the training tasks, so both methods find a safe path. In (j), there is no risk along the horizontal path, so CAT chooses that path, rather than starting in a vertical direction.
  • ...and 2 more figures

Theorems & Definitions (25)

  • Theorem 1
  • Corollary 1
  • Lemma 1
  • proof : Proof of Optimal-Optimal
  • Lemma 2
  • proof
  • Lemma 3
  • proof
  • Lemma 4
  • proof
  • ...and 15 more