In this paper, we aim to utilize only offline trajectory data to train a
policy for multi-objective RL. We extend the offline policy-regularized method,
a widely-adopted approach for single-objective offline RL problems, into the
multi-objective setting in order to achieve the above goal. However, such
methods face a new challenge in offline MORL settings, namely the
preference-inconsistent demonstration problem. We propose two solutions to this
problem: 1) filtering out preference-inconsistent demonstrations via
approximating behavior preferences, and 2) adopting regularization techniques
with high policy expressiveness. Moreover, we integrate the
preference-conditioned scalarized update method into policy-regularized offline
RL, in order to simultaneously learn a set of policies using a single policy
network, thus reducing the computational cost induced by the training of a
large number of individual policies for various preferences. Finally, we
introduce Regularization Weight Adaptation to dynamically determine appropriate
regularization weights for arbitrary target preferences during deployment.
Empirical results on various multi-objective datasets demonstrate the
capability of our approach in solving offline MORL problems.
Offline Trajectory Data and Multi-Objective Reinforcement Learning
In this paper, the focus is on utilizing offline trajectory data to train a policy for multi-objective reinforcement learning (MORL) tasks. MORL involves optimizing multiple objectives simultaneously, which adds complexity to the learning process. By extending the offline policy-regularized method, commonly used for single-objective offline RL, to the multi-objective setting, the authors aim to address this challenge.
The Challenge of Preference-Inconsistent Demonstrations
One of the main challenges in offline MORL settings is the presence of preference-inconsistent demonstrations. This refers to situations where the demonstrations provided by humans exhibit conflicting preferences for different objectives. This can make it difficult for the learning algorithm to effectively learn and generalize from the demonstrated trajectories.
The paper proposes two solutions to this problem. The first approach involves filtering out preference-inconsistent demonstrations by approximating behavior preferences. By identifying and excluding demonstrations that do not align with consistent preferences, the learning algorithm can focus on more informative examples.
The second solution involves adopting regularization techniques with high policy expressiveness. Regularization techniques help prevent overfitting and encourage generalization in machine learning models. By using regularization techniques that allow for more flexibility in expressing policies, the algorithm can better capture the complex trade-offs between multiple objectives.
Policy-Regularized Offline RL and Preference-Conditioned Scalarized Update
The paper also integrates the preference-conditioned scalarized update method into policy-regularized offline RL. This involves learning a set of policies using a single policy network, which reduces computational costs compared to training individual policies for each preference separately.
The preference-conditioned scalarized update method allows for simultaneous learning of multiple policies, each optimized for different preferences. This helps in finding a diverse set of policies that can handle various trade-offs between objectives. By using a single policy network, the algorithm can take advantage of shared knowledge across different policies.
Regularization Weight Adaptation for Arbitrary Target Preferences
To further enhance the applicability of the approach during deployment, the paper introduces Regularization Weight Adaptation. This technique dynamically determines appropriate regularization weights for arbitrary target preferences. This adaptability allows the algorithm to adjust its learning based on specific user preferences or changing task requirements.
Empirical Results and Conclusion
The paper presents empirical results on various multi-objective datasets to demonstrate the capability of their approach in solving offline MORL problems. The experiments provide evidence of the effectiveness of the proposed methods in handling preference-inconsistent demonstrations and learning policies that balance multiple objectives.
Overall, this paper highlights the multi-disciplinary nature of the concepts discussed. It combines techniques from reinforcement learning, regularization, and preference modeling to address challenges in multi-objective learning. By leveraging offline trajectory data, the approach has the potential to impact various domains such as robotics, autonomous systems, and decision-making systems where optimizing multiple objectives is critical.