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Title: Pareto Policy Adaptation
We present a policy gradient method for Multi-Objective Reinforcement Learning under unknown, linear preferences. By enforcing Pareto stationarity, a first-order condition for Pareto optimality, we are able to design a simple policy gradient al- gorithm that approximates the Pareto front and infers the unknown preferences. Our method relies on a projected gradient descent solver that identifies common ascent directions for all objectives. Leveraging the solution of that solver, we in- troduce Pareto Policy Adaptation (PPA), a loss function that adapts the policy to be optimal with respect to any distribution over preferences. PPA uses implicit differentiation to back-propagate the loss gradient bypassing the operations of the projected gradient descent solver. Our approach is straightforward, easy to imple- ment and can be used with all existing policy gradient and actor-critic methods. We evaluate our method in a series of reinforcement learning tasks.  more » « less
Award ID(s):
1932620
PAR ID:
10380951
Author(s) / Creator(s):
Date Published:
Journal Name:
International Conference on Learning Representations
Volume:
2022
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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