Dueling RL: Reinforcement Learning with Trajectory Preferences

Aadirupa Saha, Aldo Pacchiano, Jonathan Lee
Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, PMLR 206:6263-6289, 2023.

Abstract

We consider the problem of preference-based reinforcement learning (PbRL), where, unlike traditional reinforcement learning (RL), an agent receives feedback only in terms of 1 bit (0/1) preferences over a trajectory pair instead of absolute rewards for it. The success of the traditional reward-based RL framework crucially depends on how accurately a system designer can express an appropriate reward function, which is often a non-trivial task. The main novelty of the our framework is the ability to learn from preference-based trajectory feedback that eliminates the need to hand-craft numeric reward models. This paper sets up a formal framework for the PbRL problem with non-markovian rewards, where the trajectory preferences are encoded by a generalized linear model of dimension $d$. Assuming the transition model is known, we propose an algorithm with a regret guarantee of $\tilde {\mathcal{O}}\left( SH d \log (T / \delta) \sqrt{T} \right)$. We further extend the above algorithm to the case of unknown transition dynamics and provide an algorithm with regret $\widetilde{\mathcal{O}}((\sqrt{d} + H^2 + |\mathcal{S}|)\sqrt{dT} +\sqrt{|\mathcal{S}||\mathcal{A}|TH} )$. To the best of our knowledge, our work is one of the first to give tight regret guarantees for preference-based RL problem with trajectory preferences.

Cite this Paper

BibTeX
@InProceedings{pmlr-v206-saha23a, title = {Dueling RL: Reinforcement Learning with Trajectory Preferences}, author = {Saha, Aadirupa and Pacchiano, Aldo and Lee, Jonathan}, booktitle = {Proceedings of The 26th International Conference on Artificial Intelligence and Statistics}, pages = {6263--6289}, year = {2023}, editor = {Ruiz, Francisco and Dy, Jennifer and van de Meent, Jan-Willem}, volume = {206}, series = {Proceedings of Machine Learning Research}, month = {25--27 Apr}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v206/saha23a/saha23a.pdf}, url = {https://proceedings.mlr.press/v206/saha23a.html}, abstract = {We consider the problem of preference-based reinforcement learning (PbRL), where, unlike traditional reinforcement learning (RL), an agent receives feedback only in terms of 1 bit (0/1) preferences over a trajectory pair instead of absolute rewards for it. The success of the traditional reward-based RL framework crucially depends on how accurately a system designer can express an appropriate reward function, which is often a non-trivial task. The main novelty of the our framework is the ability to learn from preference-based trajectory feedback that eliminates the need to hand-craft numeric reward models. This paper sets up a formal framework for the PbRL problem with non-markovian rewards, where the trajectory preferences are encoded by a generalized linear model of dimension $d$. Assuming the transition model is known, we propose an algorithm with a regret guarantee of $\tilde {\mathcal{O}}\left( SH d \log (T / \delta) \sqrt{T} \right)$. We further extend the above algorithm to the case of unknown transition dynamics and provide an algorithm with regret $\widetilde{\mathcal{O}}((\sqrt{d} + H^2 + |\mathcal{S}|)\sqrt{dT} +\sqrt{|\mathcal{S}||\mathcal{A}|TH} )$. To the best of our knowledge, our work is one of the first to give tight regret guarantees for preference-based RL problem with trajectory preferences.} }
Endnote
%0 Conference Paper %T Dueling RL: Reinforcement Learning with Trajectory Preferences %A Aadirupa Saha %A Aldo Pacchiano %A Jonathan Lee %B Proceedings of The 26th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2023 %E Francisco Ruiz %E Jennifer Dy %E Jan-Willem van de Meent %F pmlr-v206-saha23a %I PMLR %P 6263--6289 %U https://proceedings.mlr.press/v206/saha23a.html %V 206 %X We consider the problem of preference-based reinforcement learning (PbRL), where, unlike traditional reinforcement learning (RL), an agent receives feedback only in terms of 1 bit (0/1) preferences over a trajectory pair instead of absolute rewards for it. The success of the traditional reward-based RL framework crucially depends on how accurately a system designer can express an appropriate reward function, which is often a non-trivial task. The main novelty of the our framework is the ability to learn from preference-based trajectory feedback that eliminates the need to hand-craft numeric reward models. This paper sets up a formal framework for the PbRL problem with non-markovian rewards, where the trajectory preferences are encoded by a generalized linear model of dimension $d$. Assuming the transition model is known, we propose an algorithm with a regret guarantee of $\tilde {\mathcal{O}}\left( SH d \log (T / \delta) \sqrt{T} \right)$. We further extend the above algorithm to the case of unknown transition dynamics and provide an algorithm with regret $\widetilde{\mathcal{O}}((\sqrt{d} + H^2 + |\mathcal{S}|)\sqrt{dT} +\sqrt{|\mathcal{S}||\mathcal{A}|TH} )$. To the best of our knowledge, our work is one of the first to give tight regret guarantees for preference-based RL problem with trajectory preferences.
APA
Saha, A., Pacchiano, A. & Lee, J.. (2023). Dueling RL: Reinforcement Learning with Trajectory Preferences. Proceedings of The 26th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 206:6263-6289 Available from https://proceedings.mlr.press/v206/saha23a.html.

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