Efficient Transformers in Reinforcement Learning using Actor-Learner Distillation

• URL: http://arxiv.org/abs/2104.01655v1
• Date: Sun, 4 Apr 2021 17:56:34 GMT
• Title: Efficient Transformers in Reinforcement Learning using Actor-Learner Distillation
• Authors: Emilio Parisotto, Ruslan Salakhutdinov
• Abstract summary: "Actor-Learner Distillation" transfers learning progress from a large capacity learner model to a small capacity actor model. We demonstrate in several challenging memory environments that using Actor-Learner Distillation recovers the clear sample-efficiency gains of the transformer learner model.
• Score: 91.05073136215886
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Many real-world applications such as robotics provide hard constraints on power and compute that limit the viable model complexity of Reinforcement Learning (RL) agents. Similarly, in many distributed RL settings, acting is done on un-accelerated hardware such as CPUs, which likewise restricts model size to prevent intractable experiment run times. These "actor-latency" constrained settings present a major obstruction to the scaling up of model complexity that has recently been extremely successful in supervised learning. To be able to utilize large model capacity while still operating within the limits imposed by the system during acting, we develop an "Actor-Learner Distillation" (ALD) procedure that leverages a continual form of distillation that transfers learning progress from a large capacity learner model to a small capacity actor model. As a case study, we develop this procedure in the context of partially-observable environments, where transformer models have had large improvements over LSTMs recently, at the cost of significantly higher computational complexity. With transformer models as the learner and LSTMs as the actor, we demonstrate in several challenging memory environments that using Actor-Learner Distillation recovers the clear sample-efficiency gains of the transformer learner model while maintaining the fast inference and reduced total training time of the LSTM actor model.

Related papers

• Transformer Layer Injection: A Novel Approach for Efficient Upscaling of Large Language Models [0.0]
  Transformer Layer Injection (TLI) is a novel method for efficiently upscaling large language models (LLMs) Our approach improves upon the conventional Depth Up-Scaling (DUS) technique by injecting new layers into every set of K layers.
  arXiv  Detail & Related papers  (2024-10-15T14:41:44Z)
• Adv-KD: Adversarial Knowledge Distillation for Faster Diffusion Sampling [2.91204440475204]
  Diffusion Probabilistic Models (DPMs) have emerged as a powerful class of deep generative models. They rely on sequential denoising steps during sample generation. We propose a novel method that integrates denoising phases directly into the model's architecture.
  arXiv  Detail & Related papers  (2024-05-31T08:19:44Z)
• 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)
• One-Step Diffusion Distillation via Deep Equilibrium Models [64.11782639697883]
  We introduce a simple yet effective means of distilling diffusion models directly from initial noise to the resulting image. Our method enables fully offline training with just noise/image pairs from the diffusion model. We demonstrate that the DEQ architecture is crucial to this capability, as GET matches a $5times$ larger ViT in terms of FID scores.
  arXiv  Detail & Related papers  (2023-12-12T07:28:40Z)
• Transfer of Reinforcement Learning-Based Controllers from Model- to Hardware-in-the-Loop [1.8218298349840023]
  Reinforcement Learning has great potential for autonomously training agents to perform complex control tasks. To use RL effectively in embedded system function development, the generated agents must be able to handle real-world applications. This work focuses on accelerating the training process of RL agents by combining Transfer Learning (TL) and X-in-the-Loop (XiL) simulation.
  arXiv  Detail & Related papers  (2023-10-25T09:13:12Z)
• Emergent Agentic Transformer from Chain of Hindsight Experience [96.56164427726203]
  We show that a simple transformer-based model performs competitively with both temporal-difference and imitation-learning-based approaches. This is the first time that a simple transformer-based model performs competitively with both temporal-difference and imitation-learning-based approaches.
  arXiv  Detail & Related papers  (2023-05-26T00:43:02Z)
• Learning to Grow Pretrained Models for Efficient Transformer Training [72.20676008625641]
  We learn to grow pretrained transformers, where we learn to linearly map the parameters of the smaller model to initialize the larger model. Experiments across both language and vision transformers demonstrate that our learned Linear Growth Operator (LiGO) can save up to 50% computational cost of training from scratch.
  arXiv  Detail & Related papers  (2023-03-02T05:21:18Z)
• Learning a model is paramount for sample efficiency in reinforcement learning control of PDEs [5.488334211013093]
  We show that learning an actuated model in parallel to training the RL agent significantly reduces the total amount of required data sampled from the real system. We also show that iteratively updating the model is of major importance to avoid biases in the RL training.
  arXiv  Detail & Related papers  (2023-02-14T16:14:39Z)
• Unifying Synergies between Self-supervised Learning and Dynamic Computation [53.66628188936682]
  We present a novel perspective on the interplay between SSL and DC paradigms. We show that it is feasible to simultaneously learn a dense and gated sub-network from scratch in a SSL setting. The co-evolution during pre-training of both dense and gated encoder offers a good accuracy-efficiency trade-off.
  arXiv  Detail & Related papers  (2023-01-22T17:12:58Z)
• RLFlow: Optimising Neural Network Subgraph Transformation with World Models [0.0]
  We propose a model-based agent which learns to optimise the architecture of neural networks by performing a sequence of subgraph transformations to reduce model runtime. We show our approach can match the performance of state of the art on common convolutional networks and outperform those by up to 5% on transformer-style architectures.
  arXiv  Detail & Related papers  (2022-05-03T11:52:54Z)

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.