Analyzing Generalization in Policy Networks: A Case Study with the Double-Integrator System

• URL: http://arxiv.org/abs/2312.10472v2
• Date: Sun, 31 Dec 2023 11:05:48 GMT
• Title: Analyzing Generalization in Policy Networks: A Case Study with the Double-Integrator System
• Authors: Ruining Zhang, Haoran Han, Maolong Lv, Qisong Yang, Jian Cheng
• Abstract summary: This paper uses a novel analysis technique known as state division to uncover the underlying factors contributing to performance deterioration. We show that the expansion of state space induces the activation function $tanh$ to exhibit saturability, resulting in the transformation of the state division boundary from nonlinear to linear.
• Score: 13.012569626941062
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Extensive utilization of deep reinforcement learning (DRL) policy networks in diverse continuous control tasks has raised questions regarding performance degradation in expansive state spaces where the input state norm is larger than that in the training environment. This paper aims to uncover the underlying factors contributing to such performance deterioration when dealing with expanded state spaces, using a novel analysis technique known as state division. In contrast to prior approaches that employ state division merely as a post-hoc explanatory tool, our methodology delves into the intrinsic characteristics of DRL policy networks. Specifically, we demonstrate that the expansion of state space induces the activation function $\tanh$ to exhibit saturability, resulting in the transformation of the state division boundary from nonlinear to linear. Our analysis centers on the paradigm of the double-integrator system, revealing that this gradual shift towards linearity imparts a control behavior reminiscent of bang-bang control. However, the inherent linearity of the division boundary prevents the attainment of an ideal bang-bang control, thereby introducing unavoidable overshooting. Our experimental investigations, employing diverse RL algorithms, establish that this performance phenomenon stems from inherent attributes of the DRL policy network, remaining consistent across various optimization algorithms.

Related papers

• Domain Adaptation and Entanglement: an Optimal Transport Perspective [86.24617989187988]
  Current machine learning systems are brittle in the face of distribution shifts (DS), where the target distribution that the system is tested on differs from the source distribution used to train the system. For deep neural networks, a popular framework for unsupervised domain adaptation (UDA) is domain matching, in which algorithms try to align the marginal distributions in the feature or output space. In this paper, we derive new bounds based on optimal transport that analyze the UDA problem.
  arXiv  Detail & Related papers  (2025-03-11T08:10:03Z)
• Behavior-Regularized Diffusion Policy Optimization for Offline Reinforcement Learning [22.333460316347264]
  We introduce BDPO, a principled behavior-regularized RL framework tailored for diffusion-based policies. We develop an efficient two-time-scale actor-critic RL algorithm that produces the optimal policy while respecting the behavior constraint.
  arXiv  Detail & Related papers  (2025-02-07T09:30:35Z)
• Diffusion-based Reinforcement Learning via Q-weighted Variational Policy Optimization [55.97310586039358]
  Diffusion models have garnered widespread attention in Reinforcement Learning (RL) for their powerful expressiveness and multimodality. We propose a novel model-free diffusion-based online RL algorithm, Q-weighted Variational Policy Optimization (QVPO) Specifically, we introduce the Q-weighted variational loss, which can be proved to be a tight lower bound of the policy objective in online RL under certain conditions. We also develop an efficient behavior policy to enhance sample efficiency by reducing the variance of the diffusion policy during online interactions.
  arXiv  Detail & Related papers  (2024-05-25T10:45:46Z)
• State-Constrained Offline Reinforcement Learning [9.38848713730931]
  We introduce state-constrained offline RL, a novel framework that focuses solely on the dataset's state distribution.<n>We also introduce StaCQ, a deep learning algorithm that achieves state-of-the-art performance on the D4RL benchmark datasets.
  arXiv  Detail & Related papers  (2024-05-23T09:50:04Z)
• DPO: A Differential and Pointwise Control Approach to Reinforcement Learning [3.2857981869020327]
  Reinforcement learning (RL) in continuous state-action spaces remains challenging in scientific computing.<n>We introduce Differential Reinforcement Learning (Differential RL), a novel framework that reformulates RL from a continuous-time control perspective.<n>We develop Differential Policy Optimization (DPO), a pointwise, stage-wise algorithm that refines local movement operators.
  arXiv  Detail & Related papers  (2024-04-24T03:11:12Z)
• Closed-form congestion control via deep symbolic regression [1.5961908901525192]
  Reinforcement Learning (RL) algorithms can handle challenges in ultra-low-latency and high throughput scenarios. The adoption of neural network models in real deployments still poses some challenges regarding real-time inference and interpretability. This paper proposes a methodology to deal with such challenges while maintaining the performance and generalization capabilities.
  arXiv  Detail & Related papers  (2024-03-28T14:31:37Z)
• Discovering Behavioral Modes in Deep Reinforcement Learning Policies Using Trajectory Clustering in Latent Space [0.0]
  We introduce a new approach for investigating the behavior modes of DRL policies. Specifically, we use Pairwise Controlled Manifold Approximation Projection (PaCMAP) for dimensionality reduction and TRACLUS for trajectory clustering. Our methodology helps identify diverse behavior patterns and suboptimal choices by the policy, thus allowing for targeted improvements.
  arXiv  Detail & Related papers  (2024-02-20T11:50:50Z)
• Diffusion Policies for Out-of-Distribution Generalization in Offline Reinforcement Learning [1.9336815376402723]
  offline RL methods leverage previous experiences to learn better policies than the behavior policy used for data collection. However, offline RL algorithms face challenges in handling distribution shifts and effectively representing policies due to the lack of online interaction during training. We introduce a novel method named State Reconstruction for Diffusion Policies (SRDP), incorporating state reconstruction feature learning in the recent class of diffusion policies.
  arXiv  Detail & Related papers  (2023-07-10T17:34:23Z)
• Offline Policy Optimization in RL with Variance Regularizaton [142.87345258222942]
  We propose variance regularization for offline RL algorithms, using stationary distribution corrections. We show that by using Fenchel duality, we can avoid double sampling issues for computing the gradient of the variance regularizer. The proposed algorithm for offline variance regularization (OVAR) can be used to augment any existing offline policy optimization algorithms.
  arXiv  Detail & Related papers  (2022-12-29T18:25:01Z)
• Spectral Decomposition Representation for Reinforcement Learning [100.0424588013549]
  We propose an alternative spectral method, Spectral Decomposition Representation (SPEDER), that extracts a state-action abstraction from the dynamics without inducing spurious dependence on the data collection policy. A theoretical analysis establishes the sample efficiency of the proposed algorithm in both the online and offline settings. An experimental investigation demonstrates superior performance over current state-of-the-art algorithms across several benchmarks.
  arXiv  Detail & Related papers  (2022-08-19T19:01:30Z)
• Learning Dynamics and Generalization in Reinforcement Learning [59.530058000689884]
  We show theoretically that temporal difference learning encourages agents to fit non-smooth components of the value function early in training. We show that neural networks trained using temporal difference algorithms on dense reward tasks exhibit weaker generalization between states than randomly networks and gradient networks trained with policy methods.
  arXiv  Detail & Related papers  (2022-06-05T08:49:16Z)
• Catastrophic Interference in Reinforcement Learning: A Solution Based on Context Division and Knowledge Distillation [8.044847478961882]
  We introduce the concept of "context" into single-task reinforcement learning. We develop a novel scheme, termed as Context Division and Knowledge Distillation driven RL. Our results show that, with various replay memory capacities, CDaKD can consistently improve the performance of existing RL algorithms.
  arXiv  Detail & Related papers  (2021-09-01T12:02:04Z)
• 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)
• Discrete Action On-Policy Learning with Action-Value Critic [72.20609919995086]
  Reinforcement learning (RL) in discrete action space is ubiquitous in real-world applications, but its complexity grows exponentially with the action-space dimension. We construct a critic to estimate action-value functions, apply it on correlated actions, and combine these critic estimated action values to control the variance of gradient estimation. These efforts result in a new discrete action on-policy RL algorithm that empirically outperforms related on-policy algorithms relying on variance control techniques.
  arXiv  Detail & Related papers  (2020-02-10T04:23:09Z)

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.