Recursive Entropic Risk Optimization in Discounted MDPs: Sample Complexity Bounds with a Generative Model
📰 ArXiv cs.AI
Learn to optimize risk in discounted MDPs using recursive entropic risk measures with a generative model, and understand the sample complexity bounds for value learning and policy optimization.
Action Steps
- Define a discounted MDP with a recursive entropic risk measure using a generative model
- Compute the sample complexity bounds for value learning using the generative model
- Derive an optimal policy using the learned value function and the risk parameter
- Evaluate the performance of the learned policy in the MDP environment
- Compare the results with different risk parameters to understand the trade-off between risk and reward
Who Needs to Know This
Researchers and practitioners in reinforcement learning and risk-sensitive decision-making can benefit from this work, as it provides a framework for optimizing risk in complex environments.
Key Insight
💡 Recursive entropic risk measures can be used to optimize risk in discounted MDPs, and sample complexity bounds can be derived using a generative model.
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💡 Optimize risk in discounted MDPs using recursive entropic risk measures with a generative model! 🤖
Full Article
Title: Recursive Entropic Risk Optimization in Discounted MDPs: Sample Complexity Bounds with a Generative Model
Abstract:
arXiv:2506.00286v3 Announce Type: replace-cross Abstract: We study risk-sensitive reinforcement learning in finite discounted MDPs with recursive entropic risk measures (ERM), where the risk parameter $\beta \neq 0$ controls the agent's risk attitude: $\beta>0$ for risk-averse and $\beta<0$ for risk-seeking behavior. A generative model of the MDP is assumed to be available. Our focus is on the sample complexities of learning the optimal state-action value function (value learning) and an optimal
Abstract:
arXiv:2506.00286v3 Announce Type: replace-cross Abstract: We study risk-sensitive reinforcement learning in finite discounted MDPs with recursive entropic risk measures (ERM), where the risk parameter $\beta \neq 0$ controls the agent's risk attitude: $\beta>0$ for risk-averse and $\beta<0$ for risk-seeking behavior. A generative model of the MDP is assumed to be available. Our focus is on the sample complexities of learning the optimal state-action value function (value learning) and an optimal
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