N-Gram Induction Heads for In-Context RL: Improving Stability and Reducing Data Needs

• URL: http://arxiv.org/abs/2411.01958v1
• Date: Mon, 04 Nov 2024 10:31:03 GMT
• Title: N-Gram Induction Heads for In-Context RL: Improving Stability and Reducing Data Needs
• Authors: Ilya Zisman, Alexander Nikulin, Andrei Polubarov, Nikita Lyubaykin, Vladislav Kurenkov,
• Abstract summary: In-context learning allows models like transformers to adapt to new tasks without updating their weights. Existing in-context RL methods, such as Algorithm Distillation (AD), demand large, carefully curated datasets. In this work we integrated the n-gram induction heads into transformers for in-context RL.
• Score: 42.446740732573296
• License:
• Abstract: In-context learning allows models like transformers to adapt to new tasks from a few examples without updating their weights, a desirable trait for reinforcement learning (RL). However, existing in-context RL methods, such as Algorithm Distillation (AD), demand large, carefully curated datasets and can be unstable and costly to train due to the transient nature of in-context learning abilities. In this work we integrated the n-gram induction heads into transformers for in-context RL. By incorporating these n-gram attention patterns, we significantly reduced the data required for generalization - up to 27 times fewer transitions in the Key-to-Door environment - and eased the training process by making models less sensitive to hyperparameters. Our approach not only matches but often surpasses the performance of AD, demonstrating the potential of n-gram induction heads to enhance the efficiency of in-context RL.

Related papers

• Transformers are Minimax Optimal Nonparametric In-Context Learners [36.291980654891496]
  In-context learning of large language models has proven to be a surprisingly effective method of learning a new task from only a few demonstrative examples. We develop approximation and generalization error bounds for a transformer composed of a deep neural network and one linear attention layer. We show that sufficiently trained transformers can achieve -- and even improve upon -- the minimax optimal estimation risk in context.
  arXiv  Detail & Related papers  (2024-08-22T08:02:10Z)
• Stop Regressing: Training Value Functions via Classification for Scalable Deep RL [109.44370201929246]
  We show that training value functions with categorical cross-entropy improves performance and scalability in a variety of domains. These include: single-task RL on Atari 2600 games with SoftMoEs, multi-task RL on Atari with large-scale ResNets, robotic manipulation with Q-transformers, playing Chess without search, and a language-agent Wordle task with high-capacity Transformers.
  arXiv  Detail & Related papers  (2024-03-06T18:55:47Z)
• Diffusion-Based Neural Network Weights Generation [80.89706112736353]
  D2NWG is a diffusion-based neural network weights generation technique that efficiently produces high-performing weights for transfer learning. Our method extends generative hyper-representation learning to recast the latent diffusion paradigm for neural network weights generation. Our approach is scalable to large architectures such as large language models (LLMs), overcoming the limitations of current parameter generation techniques.
  arXiv  Detail & Related papers  (2024-02-28T08:34:23Z)
• Solving Continual Offline Reinforcement Learning with Decision Transformer [78.59473797783673]
  Continuous offline reinforcement learning (CORL) combines continuous and offline reinforcement learning. Existing methods, employing Actor-Critic structures and experience replay (ER), suffer from distribution shifts, low efficiency, and weak knowledge-sharing. We introduce multi-head DT (MH-DT) and low-rank adaptation DT (LoRA-DT) to mitigate DT's forgetting problem.
  arXiv  Detail & Related papers  (2024-01-16T16:28:32Z)
• Enhancing data efficiency in reinforcement learning: a novel imagination mechanism based on mesh information propagation [0.3729614006275886]
  We introduce a novel mesh information propagation mechanism, termed the 'Imagination Mechanism (IM)' IM enables information generated by a single sample to be effectively broadcasted to different states across episodes. To promote versatility, we extend the IM to function as a plug-and-play module that can be seamlessly and fluidly integrated into other widely adopted RL algorithms.
  arXiv  Detail & Related papers  (2023-09-25T16:03:08Z)
• Supervised Pretraining Can Learn In-Context Reinforcement Learning [96.62869749926415]
  In this paper, we study the in-context learning capabilities of transformers in decision-making problems. We introduce and study Decision-Pretrained Transformer (DPT), a supervised pretraining method where the transformer predicts an optimal action. We find that the pretrained transformer can be used to solve a range of RL problems in-context, exhibiting both exploration online and conservatism offline.
  arXiv  Detail & Related papers  (2023-06-26T17:58:50Z)
• Learning Better with Less: Effective Augmentation for Sample-Efficient Visual Reinforcement Learning [57.83232242068982]
  Data augmentation (DA) is a crucial technique for enhancing the sample efficiency of visual reinforcement learning (RL) algorithms. It remains unclear which attributes of DA account for its effectiveness in achieving sample-efficient visual RL. This work conducts comprehensive experiments to assess the impact of DA's attributes on its efficacy.
  arXiv  Detail & Related papers  (2023-05-25T15:46:20Z)
• Lean Evolutionary Reinforcement Learning by Multitasking with Importance Sampling [20.9680985132322]
  We introduce a novel neuroevolutionary multitasking (NuEMT) algorithm to transfer information from a set of auxiliary tasks to the target (full length) RL task. We demonstrate that the NuEMT algorithm data-lean evolutionary RL, reducing expensive agent-environment interaction data requirements.
  arXiv  Detail & Related papers  (2022-03-21T10:06:16Z)
• Federated Deep Reinforcement Learning for the Distributed Control of NextG Wireless Networks [16.12495409295754]
  Next Generation (NextG) networks are expected to support demanding internet tactile applications such as augmented reality and connected autonomous vehicles. Data-driven approaches can improve the ability of the network to adapt to the current operating conditions. Deep RL (DRL) has been shown to achieve good performance even in complex environments.
  arXiv  Detail & Related papers  (2021-12-07T03:13:20Z)

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.