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Improving Robustness via Risk Averse Distributional Reinforcement Learning

arXiv 2005.00585 ↗First posted 1 May 2020cs.LG stat.MLcited by 28

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Abstract (arXiv)

One major obstacle that precludes the success of reinforcement learning in real-world applications is the lack of robustness, either to model uncertainties or external disturbances, of the trained policies. Robustness is critical when the policies are trained in simulations instead of real world environment. In this work, we propose a risk-aware algorithm to learn robust policies in order to bridge the gap between simulation training and real-world implementation. Our algorithm is based on recently discovered distributional RL framework. We incorporate CVaR risk measure in sample based distributional policy gradients (SDPG) for learning risk-averse policies to achieve robustness against a range of system disturbances. We validate the robustness of risk-aware SDPG on multiple environments.

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 May 2020.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 7; 2022: 8; 2023: 6; 2024: 4; 2025: 2; 2026: 1.

28 papers.

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  1. 2021Risk-Averse Offline Reinforcement Learningcited by 65
  2. 2022Uncertainty Estimation for Language Reward Modelscited by 35
  3. 2021Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methodscited by 21
  4. 2022Bridging Distributional and Risk-sensitive Reinforcement Learning with Provable Regret Boundscited by 16
  5. 2023Soft Robust MDPs and Risk-Sensitive MDPs: Equivalence, Policy Gradient, and Sample Complexitycited by 16
  6. 2021Risk-Conditioned Distributional Soft Actor-Critic for Risk-Sensitive Navigationcited by 13
  7. 2021Exploring the Training Robustness of Distributional Reinforcement Learning against Noisy State Observationscited by 11
  8. 2022Robust Reinforcement Learning with Distributional Risk-averse formulationcited by 9
  9. 2023Estimation and Inference in Distributional Reinforcement Learningcited by 8
  10. 2024Risk-Sensitive Soft Actor-Critic for Robust Deep Reinforcement Learning under Distribution Shiftscited by 6
  11. 2021Automatic Risk Adaptation in Distributional Reinforcement Learningcited by 5
  12. 2024Utility-Based Reinforcement Learning: Unifying Single-objective and Multi-objective Reinforcement Learningcited by 4
16 more
  1. 2021Deep Reinforcement Learning for Equal Risk Pricing and Hedging under Dynamic Expectile Risk Measurescited by 3
  2. 2022Distributional Actor-Critic Ensemble for Uncertainty-Aware Continuous Controlcited by 3
  3. 2025DR-SAC: Distributionally Robust Soft Actor-Critic for Reinforcement Learning under Uncertaintycited by 3
  4. 2021ACReL: Adversarial Conditional value-at-risk Reinforcement Learningcited by 2
  5. 2022Review of Metrics to Measure the Stability, Robustness and Resilience of Reinforcement Learningcited by 2
  6. 2022Tailoring to the Tails: Risk Measures for Fine-Grained Tail Sensitivitycited by 2
  7. 2023PACER: A Fully Push-forward-based Distributional Reinforcement Learning Algorithmcited by 2
  8. 2024Diffusion Policies for Risk-Averse Behavior Modeling in Offline Reinforcement Learningcited by 2
  9. 2022Beyond Bayes-optimality: meta-learning what you know you don't knowcited by 1
  10. 2024On the Approximation and Convergence of Distributional Policy Gradient Algorithms for Risk-Sensitive Reinforcement Learningcited by 1
  11. 2022Applications of Reinforcement Learning in Deregulated Power Market: A Comprehensive Reviewcited by 0
  12. 2023Uniform Pessimistic Risk and its Optimal Portfoliocited by 0
  13. 2023Policy Evaluation in Distributional LQRcited by 0
  14. 2023DRL-ORA: Distributional Reinforcement Learning with Online Risk Adaptioncited by 0
  15. 2025Risk-averse policies for natural gas futures trading using distributional reinforcement learningcited by 0
  16. 2026Fuz-RL: A Fuzzy-Guided Robust Framework for Safe Reinforcement Learning under Uncertaintycited by 0