Policy Gradient Methods for Distortion Risk Measures
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2021
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| _version_ | 1866917581540032512 |
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| author | Vijayan, Nithia A, Prashanth L. |
| author_facet | Vijayan, Nithia A, Prashanth L. |
| contents | We propose policy gradient algorithms which learn risk-sensitive policies in a reinforcement learning (RL) framework. Our proposed algorithms maximize the distortion risk measure (DRM) of the cumulative reward in an episodic Markov decision process in on-policy and off-policy RL settings, respectively. We derive a variant of the policy gradient theorem that caters to the DRM objective, and integrate it with a likelihood ratio-based gradient estimation scheme. We derive non-asymptotic bounds that establish the convergence of our proposed algorithms to an approximate stationary point of the DRM objective. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2107_04422 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Policy Gradient Methods for Distortion Risk Measures Vijayan, Nithia A, Prashanth L. Machine Learning We propose policy gradient algorithms which learn risk-sensitive policies in a reinforcement learning (RL) framework. Our proposed algorithms maximize the distortion risk measure (DRM) of the cumulative reward in an episodic Markov decision process in on-policy and off-policy RL settings, respectively. We derive a variant of the policy gradient theorem that caters to the DRM objective, and integrate it with a likelihood ratio-based gradient estimation scheme. We derive non-asymptotic bounds that establish the convergence of our proposed algorithms to an approximate stationary point of the DRM objective. |
| title | Policy Gradient Methods for Distortion Risk Measures |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2107.04422 |