Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings

• URL: http://arxiv.org/abs/2103.02886v1
• Date: Thu, 4 Mar 2021 08:14:10 GMT
• Title: Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings
• Authors: Lili Chen, Kimin Lee, Aravind Srinivas, Pieter Abbeel
• Abstract summary: We present Stored Embeddings for Efficient Reinforcement Learning (SEER) SEER is a simple modification of existing off-policy deep reinforcement learning methods. We show that SEER does not degrade the performance of RLizable agents while significantly saving computation and memory.
• Score: 89.63764845984076
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in off-policy deep reinforcement learning (RL) have led to impressive success in complex tasks from visual observations. Experience replay improves sample-efficiency by reusing experiences from the past, and convolutional neural networks (CNNs) process high-dimensional inputs effectively. However, such techniques demand high memory and computational bandwidth. In this paper, we present Stored Embeddings for Efficient Reinforcement Learning (SEER), a simple modification of existing off-policy RL methods, to address these computational and memory requirements. To reduce the computational overhead of gradient updates in CNNs, we freeze the lower layers of CNN encoders early in training due to early convergence of their parameters. Additionally, we reduce memory requirements by storing the low-dimensional latent vectors for experience replay instead of high-dimensional images, enabling an adaptive increase in the replay buffer capacity, a useful technique in constrained-memory settings. In our experiments, we show that SEER does not degrade the performance of RL agents while significantly saving computation and memory across a diverse set of DeepMind Control environments and Atari games. Finally, we show that SEER is useful for computation-efficient transfer learning in RL because lower layers of CNNs extract generalizable features, which can be used for different tasks and domains.

Related papers

• SHERL: Synthesizing High Accuracy and Efficient Memory for Resource-Limited Transfer Learning [63.93193829913252]
  We propose an innovative METL strategy called SHERL for resource-limited scenarios. In the early route, intermediate outputs are consolidated via an anti-redundancy operation. In the late route, utilizing minimal late pre-trained layers could alleviate the peak demand on memory overhead.
  arXiv  Detail & Related papers  (2024-07-10T10:22:35Z)
• Compressing the Backward Pass of Large-Scale Neural Architectures by Structured Activation Pruning [0.0]
  Sparsity in Deep Neural Networks (DNNs) has gained attention as a solution. This work focuses on ephemeral sparsity, aiming to reduce memory consumption during training. We report the effectiveness of activation pruning by evaluating training speed, accuracy, and memory usage of large-scale neural architectures.
  arXiv  Detail & Related papers  (2023-11-28T15:31:31Z)
• Reinforcement Learning with Fast and Forgetful Memory [10.087126455388276]
  We introduce Fast and Forgetful Memory, an algorithm-agnostic memory model designed specifically for Reinforcement Learning (RL) Our approach constrains the model search space via strong structural priors inspired by computational psychology. Fast and Forgetful Memory exhibits training speeds two orders of magnitude faster than recurrent neural networks (RNNs)
  arXiv  Detail & Related papers  (2023-10-06T09:56:26Z)
• A Memory Efficient Deep Reinforcement Learning Approach For Snake Game Autonomous Agents [0.799536002595393]
  This paper presents a modified DRL method that performs reasonably well with compressed imagery data without requiring additional environment information. We have designed a lightweight Convolutional Neural Network (CNN) with a variant of the Q-network that efficiently takes preprocessed image data as input and uses less memory.
  arXiv  Detail & Related papers  (2023-01-27T20:26:48Z)
• Memory-efficient Reinforcement Learning with Value-based Knowledge Consolidation [14.36005088171571]
  We propose memory-efficient reinforcement learning algorithms based on the deep Q-network algorithm. Our algorithms reduce forgetting and maintain high sample efficiency by consolidating knowledge from the target Q-network to the current Q-network.
  arXiv  Detail & Related papers  (2022-05-22T17:02:51Z)
• Online Convolutional Re-parameterization [51.97831675242173]
  We present online convolutional re- parameterization (OREPA), a two-stage pipeline, aiming to reduce the huge training overhead by squeezing the complex training-time block into a single convolution. Compared with the state-of-the-art re-param models, OREPA is able to save the training-time memory cost by about 70% and accelerate the training speed by around 2x. We also conduct experiments on object detection and semantic segmentation and show consistent improvements on the downstream tasks.
  arXiv  Detail & Related papers  (2022-04-02T09:50:19Z)
• Learning Bayesian Sparse Networks with Full Experience Replay for Continual Learning [54.7584721943286]
  Continual Learning (CL) methods aim to enable machine learning models to learn new tasks without catastrophic forgetting of those that have been previously mastered. Existing CL approaches often keep a buffer of previously-seen samples, perform knowledge distillation, or use regularization techniques towards this goal. We propose to only activate and select sparse neurons for learning current and past tasks at any stage.
  arXiv  Detail & Related papers  (2022-02-21T13:25:03Z)
• Mesa: A Memory-saving Training Framework for Transformers [58.78933015299703]
  We present Mesa, a memory-saving training framework for Transformers. Mesa uses exact activations during forward pass while storing a low-precision version of activations to reduce memory consumption during training. Experiments on ImageNet, CIFAR-100 and ADE20K demonstrate that Mesa can reduce half of the memory footprints during training.
  arXiv  Detail & Related papers  (2021-11-22T11:23:01Z)
• Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning [56.83172249278467]
  We introduce Evolutionary Graph Reinforcement Learning (EGRL), a method designed for large search spaces. We train and validate our approach directly on the Intel NNP-I chip for inference. We additionally achieve 28-78% speed-up compared to the native NNP-I compiler on all three workloads.
  arXiv  Detail & Related papers  (2020-07-14T18:50:12Z)

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.