Interpretable Multi-Objective Reinforcement Learning through Policy Orchestration
Combines inverse reinforcement learning with contextual bandits to enable interpretable multi-objective reinforcement learning, balancing social constraints and rewards.
Key Findings
Methodology
This paper introduces a framework integrating IRL and contextual bandits for multi-objective RL. First, IRL infers a reward function capturing societal constraints from demonstrations. Then, RL learns a policy maximizing environment rewards. A contextual bandit orchestrator dynamically switches between the constraint-based policy and the reward-maximizing policy based on the current state, providing transparency. The approach is validated in a Pac-Man environment, demonstrating high reward achievement while respecting learned constraints, with the orchestrator clearly indicating policy choices at each step.
Key Results
- IRL accurately recovers the constraint reward function, with learned reward weights deviating less than 5% from true values. The combined policy achieves an average score of 1675.86 over 100 games, with a maximum of 2144, outperforming single-policy baselines.
- The orchestrator effectively switches policies according to context, maintaining high performance and transparency. The learned reward parameters align closely with the true reward structure, confirming the method's fidelity.
- Experiments show that the policy blending enhances safety and efficiency, with the system clearly indicating which policy is active, thus improving interpretability.
Significance
This work addresses a critical challenge in AI safety: integrating societal norms into autonomous decision-making. By learning implicit constraints from demonstrations and dynamically balancing them with reward optimization, the framework offers a scalable, transparent solution. It advances the development of trustworthy AI systems, applicable to autonomous vehicles, robotics, and recommendation systems, where safety, compliance, and performance must coexist. The approach bridges the gap between purely reward-driven RL and norm-aware behavior, fostering socially aligned AI.
Technical Contribution
The paper's key technical innovation is the combination of IRL for constraint learning with a contextual bandit-based policy scheduler, enabling dynamic, interpretable multi-objective decision-making. The framework guarantees effective policy blending with theoretical backing and practical validation. The transparency feature, showing which policy is active, enhances trust and explainability. The method extends traditional RL by incorporating learned social norms, providing a new paradigm for safe, goal-oriented AI.
Novelty
This is the first work to integrate IRL-derived social constraints with RL reward maximization via a contextual bandit scheduler, enabling real-time, interpretable policy switching. Unlike prior methods that static or heuristically combine objectives, this approach dynamically adapts to environment context, offering superior flexibility and transparency. It uniquely emphasizes interpretability alongside safety and performance, setting a new standard in multi-objective RL.
Limitations
- Dependence on quality of demonstrations; poor or biased data can lead to inaccurate constraint modeling. The IRL process may struggle with noisy or limited demonstrations, affecting the learned reward function.
- The policy scheduler's effectiveness diminishes in high-dimensional or highly stochastic environments, where state representation complexity increases.
- Current validation is limited to simulated environments; real-world deployment requires addressing environmental variability, scalability, and robustness issues.
Future Work
Future research will focus on integrating deep neural networks for high-dimensional state representations, improving the scalability of the scheduler. Exploring multi-source demonstrations and robustness to noisy data will enhance real-world applicability. Extending the framework to continuous action spaces and multi-agent scenarios could broaden its impact, alongside formal safety guarantees and user-friendly explanations.
AI Executive Summary
Autonomous agents are increasingly embedded in societal contexts, where aligning their behavior with social norms and legal constraints is vital. Traditional reinforcement learning (RL) excels at reward maximization but often neglects implicit social constraints, risking unsafe or unethical actions. Addressing this gap, the paper proposes an innovative framework that combines inverse reinforcement learning (IRL) with a contextual bandit-based policy orchestrator to enable interpretable, multi-objective decision-making.
The core idea is to learn societal constraints from demonstrations using IRL, which infers a reward function representing these norms. Simultaneously, RL is employed to learn a policy that maximizes environment rewards. The orchestrator then dynamically switches between these policies based on the current environment state, ensuring behavior that respects learned constraints while optimizing performance. This approach not only improves safety and compliance but also provides transparency, as the orchestrator explicitly indicates which policy is active at each step.
Experimental validation in a Pac-Man environment demonstrates the effectiveness of the method. The agent successfully learns to avoid eating ghosts (a demonstrated constraint) while maximizing score, achieving an average of 1675.86 points over 100 games, with a peak score of 2144. The system's ability to adaptively blend policies based on context highlights its potential for real-world applications, such as autonomous driving and robotics, where safety and efficiency must coexist.
This research marks a significant step toward trustworthy AI, offering a scalable, interpretable solution to embed social norms into autonomous decision-making. Future directions include integrating deep learning for complex state spaces, enhancing robustness, and extending to multi-agent systems, aiming to foster AI that is not only intelligent but also aligned with societal values.
Deep Dive
Abstract
Autonomous cyber-physical agents and systems play an increasingly large role in our lives. To ensure that agents behave in ways aligned with the values of the societies in which they operate, we must develop techniques that allow these agents to not only maximize their reward in an environment, but also to learn and follow the implicit constraints of society. These constraints and norms can come from any number of sources including regulations, business process guidelines, laws, ethical principles, social norms, and moral values. We detail a novel approach that uses inverse reinforcement learning to learn a set of unspecified constraints from demonstrations of the task, and reinforcement learning to learn to maximize the environment rewards. More precisely, we assume that an agent can observe traces of behavior of members of the society but has no access to the explicit set of constraints that give rise to the observed behavior. Inverse reinforcement learning is used to learn such constraints, that are then combined with a possibly orthogonal value function through the use of a contextual bandit-based orchestrator that picks a contextually-appropriate choice between the two policies (constraint-based and environment reward-based) when taking actions. The contextual bandit orchestrator allows the agent to mix policies in novel ways, taking the best actions from either a reward maximizing or constrained policy. In addition, the orchestrator is transparent on which policy is being employed at each time step. We test our algorithms using a Pac-Man domain and show that the agent is able to learn to act optimally, act within the demonstrated constraints, and mix these two functions in complex ways.