Robust Model-Based Reinforcement Learning with an Adversarial Auxiliary Model

• URL: http://arxiv.org/abs/2406.09976v2
• Date: Mon, 1 Jul 2024 13:35:44 GMT
• Title: Robust Model-Based Reinforcement Learning with an Adversarial Auxiliary Model
• Authors: Siemen Herremans, Ali Anwar, Siegfried Mercelis,
• Abstract summary: An RL agent that trains in a certain Markov decision process (MDP) often struggles to perform well in nearly identical MDPs. We employ the framework of Robust MDPs in a model-based setting and introduce a novel learned transition model. Our experimental results indicate a notable improvement in policy robustness on high-dimensional MuJoCo control tasks.
• Score: 2.9109581496560044
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reinforcement learning has demonstrated impressive performance in various challenging problems such as robotics, board games, and classical arcade games. However, its real-world applications can be hindered by the absence of robustness and safety in the learned policies. More specifically, an RL agent that trains in a certain Markov decision process (MDP) often struggles to perform well in nearly identical MDPs. To address this issue, we employ the framework of Robust MDPs (RMDPs) in a model-based setting and introduce a novel learned transition model. Our method specifically incorporates an auxiliary pessimistic model, updated adversarially, to estimate the worst-case MDP within a Kullback-Leibler uncertainty set. In comparison to several existing works, our work does not impose any additional conditions on the training environment, such as the need for a parametric simulator. To test the effectiveness of the proposed pessimistic model in enhancing policy robustness, we integrate it into a practical RL algorithm, called Robust Model-Based Policy Optimization (RMBPO). Our experimental results indicate a notable improvement in policy robustness on high-dimensional MuJoCo control tasks, with the auxiliary model enhancing the performance of the learned policy in distorted MDPs. We further explore the learned deviation between the proposed auxiliary world model and the nominal model, to examine how pessimism is achieved. By learning a pessimistic world model and demonstrating its role in improving policy robustness, our research contributes towards making (model-based) RL more robust.

Related papers

• Safe Deep Model-Based Reinforcement Learning with Lyapunov Functions [2.50194939587674]
  We propose a new Model-based RL framework to enable efficient policy learning with unknown dynamics. We introduce and explore a novel method for adding safety constraints for model-based RL during training and policy learning.
  arXiv  Detail & Related papers  (2024-05-25T11:21:12Z)
• Reparameterized Policy Learning for Multimodal Trajectory Optimization [61.13228961771765]
  We investigate the challenge of parametrizing policies for reinforcement learning in high-dimensional continuous action spaces. We propose a principled framework that models the continuous RL policy as a generative model of optimal trajectories. We present a practical model-based RL method, which leverages the multimodal policy parameterization and learned world model.
  arXiv  Detail & Related papers  (2023-07-20T09:05:46Z)
• Predictable MDP Abstraction for Unsupervised Model-Based RL [93.91375268580806]
  We propose predictable MDP abstraction (PMA) Instead of training a predictive model on the original MDP, we train a model on a transformed MDP with a learned action space. We theoretically analyze PMA and empirically demonstrate that PMA leads to significant improvements over prior unsupervised model-based RL approaches.
  arXiv  Detail & Related papers  (2023-02-08T07:37:51Z)
• When to Update Your Model: Constrained Model-based Reinforcement Learning [50.74369835934703]
  We propose a novel and general theoretical scheme for a non-decreasing performance guarantee of model-based RL (MBRL) Our follow-up derived bounds reveal the relationship between model shifts and performance improvement. A further example demonstrates that learning models from a dynamically-varying number of explorations benefit the eventual returns.
  arXiv  Detail & Related papers  (2022-10-15T17:57:43Z)
• Online Policy Optimization for Robust MDP [17.995448897675068]
  Reinforcement learning (RL) has exceeded human performance in many synthetic settings such as video games and Go. In this work, we consider online robust Markov decision process (MDP) by interacting with an unknown nominal system. We propose a robust optimistic policy optimization algorithm that is provably efficient.
  arXiv  Detail & Related papers  (2022-09-28T05:18:20Z)
• Value Gradient weighted Model-Based Reinforcement Learning [28.366157882991565]
  Model-based reinforcement learning (MBRL) is a sample efficient technique to obtain control policies. VaGraM is a novel method for value-aware model learning.
  arXiv  Detail & Related papers  (2022-04-04T13:28:31Z)
• Sample Complexity of Robust Reinforcement Learning with a Generative Model [0.0]
  We propose a model-based reinforcement learning (RL) algorithm for learning an $epsilon$-optimal robust policy. We consider three different forms of uncertainty sets, characterized by the total variation distance, chi-square divergence, and KL divergence. In addition to the sample complexity results, we also present a formal analytical argument on the benefit of using robust policies.
  arXiv  Detail & Related papers  (2021-12-02T18:55:51Z)
• Pessimistic Model Selection for Offline Deep Reinforcement Learning [56.282483586473816]
  Deep Reinforcement Learning (DRL) has demonstrated great potentials in solving sequential decision making problems in many applications. One main barrier is the over-fitting issue that leads to poor generalizability of the policy learned by DRL. We propose a pessimistic model selection (PMS) approach for offline DRL with a theoretical guarantee.
  arXiv  Detail & Related papers  (2021-11-29T06:29:49Z)
• Evaluating model-based planning and planner amortization for continuous control [79.49319308600228]
  We take a hybrid approach, combining model predictive control (MPC) with a learned model and model-free policy learning. We find that well-tuned model-free agents are strong baselines even for high DoF control problems. We show that it is possible to distil a model-based planner into a policy that amortizes the planning without any loss of performance.
  arXiv  Detail & Related papers  (2021-10-07T12:00:40Z)
• Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models [40.08137765886609]
  We show that our model, called a graph structured surrogate model (GSSM), outperforms state-of-the-art methods in predicting environment dynamics. Our approach is able to obtain high returns, while allowing fast execution during deployment by avoiding test time policy gradient optimization.
  arXiv  Detail & Related papers  (2021-02-16T17:21:55Z)
• On the model-based stochastic value gradient for continuous reinforcement learning [50.085645237597056]
  We show that simple model-based agents can outperform state-of-the-art model-free agents in terms of both sample-efficiency and final reward. Our findings suggest that model-based policy evaluation deserves closer attention.
  arXiv  Detail & Related papers  (2020-08-28T17:58:29Z)

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.