Robust Reinforcement Learning in Continuous Control Tasks with
Uncertainty Set Regularization
- URL: http://arxiv.org/abs/2207.02016v4
- Date: Tue, 5 Dec 2023 13:44:04 GMT
- Title: Robust Reinforcement Learning in Continuous Control Tasks with
Uncertainty Set Regularization
- Authors: Yuan Zhang, Jianhong Wang, Joschka Boedecker
- Abstract summary: Reinforcement learning (RL) is recognized as lacking generalization and robustness under environmental perturbations.
We propose a new regularizer named $textbfU$ncertainty $textbfS$et $textbfR$egularizer (USR)
- Score: 17.322284328945194
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Reinforcement learning (RL) is recognized as lacking generalization and
robustness under environmental perturbations, which excessively restricts its
application for real-world robotics. Prior work claimed that adding
regularization to the value function is equivalent to learning a robust policy
with uncertain transitions. Although the regularization-robustness
transformation is appealing for its simplicity and efficiency, it is still
lacking in continuous control tasks. In this paper, we propose a new
regularizer named $\textbf{U}$ncertainty $\textbf{S}$et $\textbf{R}$egularizer
(USR), by formulating the uncertainty set on the parameter space of the
transition function. In particular, USR is flexible enough to be plugged into
any existing RL framework. To deal with unknown uncertainty sets, we further
propose a novel adversarial approach to generate them based on the value
function. We evaluate USR on the Real-world Reinforcement Learning (RWRL)
benchmark, demonstrating improvements in the robust performance for perturbed
testing environments.
Related papers
- RCR-AF: Enhancing Model Generalization via Rademacher Complexity Reduction Activation Function [28.98307539597017]
This paper investigates activation functions as a crucial yet underexplored component for enhancing model robustness.<n>We propose a Rademacher Complexity Reduction Activation Function (RCR-AF), a novel activation function designed to improve both generalization and adversarial resilience.
arXiv Detail & Related papers (2025-07-30T07:45:03Z) - Safety-Aware Reinforcement Learning for Control via Risk-Sensitive Action-Value Iteration and Quantile Regression [2.592761128203891]
Quantile-based action-value iteration methods reduce this bias by learning a distribution of the expected cost-to-go.<n>Existing methods often require complex neural architectures or manual tradeoffs due to combined cost functions.<n>We propose a risk-regularized quantile-based algorithm integrating Conditional Value-at-Risk to enforce safety without complex architectures.
arXiv Detail & Related papers (2025-06-08T00:22:00Z) - Robust Offline Reinforcement Learning with Linearly Structured $f$-Divergence Regularization [10.465789490644031]
We propose a novel framework for robust regularized Markov decision process ($d$-RRMDP)
For the offline RL setting, we develop a family of algorithms, Robust Regularized Pessimistic Value Iteration (R2PVI)
arXiv Detail & Related papers (2024-11-27T18:57:03Z) - Natural Actor-Critic for Robust Reinforcement Learning with Function
Approximation [20.43657369407846]
We study robust reinforcement learning (RL) with the goal of determining a well-performing policy that is robust against model mismatch between the training simulator and the testing environment.
We propose two novel uncertainty set formulations, one based on double sampling and the other on an integral probability metric.
We demonstrate the robust performance of the policy learned by our proposed RNAC approach in multiple MuJoCo environments and a real-world TurtleBot navigation task.
arXiv Detail & Related papers (2023-07-17T22:10:20Z) - A Multiplicative Value Function for Safe and Efficient Reinforcement
Learning [131.96501469927733]
We propose a safe model-free RL algorithm with a novel multiplicative value function consisting of a safety critic and a reward critic.
The safety critic predicts the probability of constraint violation and discounts the reward critic that only estimates constraint-free returns.
We evaluate our method in four safety-focused environments, including classical RL benchmarks augmented with safety constraints and robot navigation tasks with images and raw Lidar scans as observations.
arXiv Detail & Related papers (2023-03-07T18:29:15Z) - AutoCost: Evolving Intrinsic Cost for Zero-violation Reinforcement
Learning [3.4806267677524896]
We propose AutoCost, a framework that automatically searches for cost functions that help constrained RL to achieve zero-violation performance.
We compare the performance of augmented agents that use our cost function to provide additive intrinsic costs with baseline agents that use the same policy learners but with only extrinsic costs.
arXiv Detail & Related papers (2023-01-24T22:51:29Z) - FIRE: A Failure-Adaptive Reinforcement Learning Framework for Edge Computing Migrations [52.85536740465277]
FIRE is a framework that adapts to rare events by training a RL policy in an edge computing digital twin environment.
We propose ImRE, an importance sampling-based Q-learning algorithm, which samples rare events proportionally to their impact on the value function.
We show that FIRE reduces costs compared to vanilla RL and the greedy baseline in the event of failures.
arXiv Detail & Related papers (2022-09-28T19:49:39Z) - RORL: Robust Offline Reinforcement Learning via Conservative Smoothing [72.8062448549897]
offline reinforcement learning can exploit the massive amount of offline data for complex decision-making tasks.
Current offline RL algorithms are generally designed to be conservative for value estimation and action selection.
We propose Robust Offline Reinforcement Learning (RORL) with a novel conservative smoothing technique.
arXiv Detail & Related papers (2022-06-06T18:07:41Z) - Policy Mirror Descent for Regularized Reinforcement Learning: A
Generalized Framework with Linear Convergence [60.20076757208645]
This paper proposes a general policy mirror descent (GPMD) algorithm for solving regularized RL.
We demonstrate that our algorithm converges linearly over an entire range learning rates, in a dimension-free fashion, to the global solution.
arXiv Detail & Related papers (2021-05-24T02:21:34Z) - Closing the Closed-Loop Distribution Shift in Safe Imitation Learning [80.05727171757454]
We treat safe optimization-based control strategies as experts in an imitation learning problem.
We train a learned policy that can be cheaply evaluated at run-time and that provably satisfies the same safety guarantees as the expert.
arXiv Detail & Related papers (2021-02-18T05:11:41Z) - Deep Reinforcement Learning with Robust and Smooth Policy [90.78795857181727]
We propose to learn a smooth policy that behaves smoothly with respect to states.
We develop a new framework -- textbfSmooth textbfRegularized textbfReinforcement textbfLearning ($textbfSR2textbfL$), where the policy is trained with smoothness-inducing regularization.
Such regularization effectively constrains the search space, and enforces smoothness in the learned policy.
arXiv Detail & Related papers (2020-03-21T00:10:29Z) - Distributional Robustness and Regularization in Reinforcement Learning [62.23012916708608]
We introduce a new regularizer for empirical value functions and show that it lower bounds the Wasserstein distributionally robust value function.
It suggests using regularization as a practical tool for dealing with $textitexternal uncertainty$ in reinforcement learning.
arXiv Detail & Related papers (2020-03-05T19:56:23Z) - Guided Constrained Policy Optimization for Dynamic Quadrupedal Robot
Locomotion [78.46388769788405]
We introduce guided constrained policy optimization (GCPO), an RL framework based upon our implementation of constrained policy optimization (CPPO)
We show that guided constrained RL offers faster convergence close to the desired optimum resulting in an optimal, yet physically feasible, robotic control behavior without the need for precise reward function tuning.
arXiv Detail & Related papers (2020-02-22T10:15:53Z) - Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for
Addressing Value Estimation Errors [13.534873779043478]
We present a distributional soft actor-critic (DSAC) algorithm to improve the policy performance by mitigating Q-value overestimations.
We evaluate DSAC on the suite of MuJoCo continuous control tasks, achieving the state-of-the-art performance.
arXiv Detail & Related papers (2020-01-09T02:27:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.