An Empirical Study on the Power of Future Prediction in Partially Observable Environments

• URL: http://arxiv.org/abs/2402.07102v2
• Date: Sat, 08 Mar 2025 04:14:42 GMT
• Title: An Empirical Study on the Power of Future Prediction in Partially Observable Environments
• Authors: Jeongyeol Kwon, Liu Yang, Robert Nowak, Josiah Hanna,
• Abstract summary: Self-predictive auxiliary tasks have been shown to improve performance in fully observed settings, but their role in partial observability remains underexplored.<n>We introduce $textttDRL2$, an approach that explicitly decouples representation learning from reinforcement learning.<n>Our findings reinforce the idea that future prediction performance serves as a reliable indicator of representation quality and contributes to improved RL performance.
• Score: 15.773444560355694
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning good representations of historical contexts is one of the core challenges of reinforcement learning (RL) in partially observable environments. While self-predictive auxiliary tasks have been shown to improve performance in fully observed settings, their role in partial observability remains underexplored. In this empirical study, we examine the effectiveness of self-predictive representation learning via future prediction, i.e., predicting next-step observations as an auxiliary task for learning history representations, especially in environments with long-term dependencies. We test the hypothesis that future prediction alone can produce representations that enable strong RL performance. To evaluate this, we introduce $\texttt{DRL}^2$, an approach that explicitly decouples representation learning from reinforcement learning, and compare this approach to end-to-end training across multiple benchmarks requiring long-term memory. Our findings provide evidence that this hypothesis holds across different network architectures, reinforcing the idea that future prediction performance serves as a reliable indicator of representation quality and contributes to improved RL performance.

Related papers

• Learning Future Representation with Synthetic Observations for Sample-efficient Reinforcement Learning [12.277005054008017]
  In visual Reinforcement Learning (RL), upstream representation learning largely determines the effect of downstream policy learning. We try to improve auxiliary representation learning for RL by enriching auxiliary training data. We propose a training-free method to synthesize observations that may contain future information. The remaining synthetic observations and real observations then serve as the auxiliary data to achieve a clustering-based temporal association task.
  arXiv  Detail & Related papers  (2024-05-20T02:43:04Z)
• Learning Interpretable Policies in Hindsight-Observable POMDPs through Partially Supervised Reinforcement Learning [57.67629402360924]
  We introduce the Partially Supervised Reinforcement Learning (PSRL) framework. At the heart of PSRL is the fusion of both supervised and unsupervised learning. We show that PSRL offers a potent balance, enhancing model interpretability while preserving, and often significantly outperforming, the performance benchmarks set by traditional methods.
  arXiv  Detail & Related papers  (2024-02-14T16:23:23Z)
• Assessing the Impact of Distribution Shift on Reinforcement Learning Performance [0.0]
  Reinforcement learning (RL) faces its own set of unique challenges. Comparison of point estimates, and plots that show successful convergence to the optimal policy during training, may obfuscate overfitting or dependence on the experimental setup. We propose a set of evaluation methods that measure the robustness of RL algorithms under distribution shifts.
  arXiv  Detail & Related papers  (2024-02-05T23:50:55Z)
• Harnessing Discrete Representations For Continual Reinforcement Learning [8.61539229796467]
  We investigate the advantages of representing observations as vectors of categorical values within the context of reinforcement learning. We find that, when compared to traditional continuous representations, world models learned over discrete representations accurately model more of the world with less capacity.
  arXiv  Detail & Related papers  (2023-12-02T18:55:26Z)
• Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality [55.88910947643436]
  Self-supervised pre-training is essential for handling vast quantities of unlabeled data in practice. HiDe-Prompt is an innovative approach that explicitly optimize the hierarchical components with an ensemble of task-specific prompts and statistics. Our experiments demonstrate the superior performance of HiDe-Prompt and its robustness to pre-training paradigms in continual learning.
  arXiv  Detail & Related papers  (2023-10-11T06:51:46Z)
• Fair Representation Learning using Interpolation Enabled Disentanglement [9.043741281011304]
  We propose a novel method to address two key issues: (a) Can we simultaneously learn fair disentangled representations while ensuring the utility of the learned representation for downstream tasks, and (b)Can we provide theoretical insights into when the proposed approach will be both fair and accurate. To address the former, we propose the method FRIED, Fair Representation learning using Interpolation Enabled Disentanglement.
  arXiv  Detail & Related papers  (2021-07-31T17:32:12Z)
• Co$^2$L: Contrastive Continual Learning [69.46643497220586]
  Recent breakthroughs in self-supervised learning show that such algorithms learn visual representations that can be transferred better to unseen tasks. We propose a rehearsal-based continual learning algorithm that focuses on continually learning and maintaining transferable representations.
  arXiv  Detail & Related papers  (2021-06-28T06:14:38Z)
• Which Mutual-Information Representation Learning Objectives are Sufficient for Control? [80.2534918595143]
  Mutual information provides an appealing formalism for learning representations of data. This paper formalizes the sufficiency of a state representation for learning and representing the optimal policy. Surprisingly, we find that two of these objectives can yield insufficient representations given mild and common assumptions on the structure of the MDP.
  arXiv  Detail & Related papers  (2021-06-14T10:12:34Z)
• Heterogeneous Contrastive Learning: Encoding Spatial Information for Compact Visual Representations [183.03278932562438]
  This paper presents an effective approach that adds spatial information to the encoding stage to alleviate the learning inconsistency between the contrastive objective and strong data augmentation operations. We show that our approach achieves higher efficiency in visual representations and thus delivers a key message to inspire the future research of self-supervised visual representation learning.
  arXiv  Detail & Related papers  (2020-11-19T16:26:25Z)
• Counterfactual Representation Learning with Balancing Weights [74.67296491574318]
  Key to causal inference with observational data is achieving balance in predictive features associated with each treatment type. Recent literature has explored representation learning to achieve this goal. We develop an algorithm for flexible, scalable and accurate estimation of causal effects.
  arXiv  Detail & Related papers  (2020-10-23T19:06:03Z)
• CLAR: Contrastive Learning of Auditory Representations [6.1424670675582576]
  We introduce various data augmentations suitable for auditory data and evaluate their impact on predictive performance. We show that training with time-frequency audio features substantially improves the quality of the learned representations. We illustrate that by combining all these methods and with substantially less labeled data, our framework (CLAR) achieves significant improvement on prediction performance.
  arXiv  Detail & Related papers  (2020-10-19T14:15:31Z)
• Video Prediction via Example Guidance [156.08546987158616]
  In video prediction tasks, one major challenge is to capture the multi-modal nature of future contents and dynamics. In this work, we propose a simple yet effective framework that can efficiently predict plausible future states.
  arXiv  Detail & Related papers  (2020-07-03T14:57:24Z)
• Deep Reinforcement and InfoMax Learning [32.426674181365456]
  We introduce an objective based on Deep InfoMax which trains the agent to predict the future by maximizing the mutual information between its internal representation of successive timesteps. We test our approach in several synthetic settings, where it successfully learns representations that are predictive of the future.
  arXiv  Detail & Related papers  (2020-06-12T14:19:46Z)
• Value-driven Hindsight Modelling [68.658900923595]
  Value estimation is a critical component of the reinforcement learning (RL) paradigm. Model learning can make use of the rich transition structure present in sequences of observations, but this approach is usually not sensitive to the reward function. We develop an approach for representation learning in RL that sits in between these two extremes. This provides tractable prediction targets that are directly relevant for a task, and can thus accelerate learning the value function.
  arXiv  Detail & Related papers  (2020-02-19T18:10:20Z)

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.