Augmented Replay Memory in Reinforcement Learning With Continuous Control

• URL: http://arxiv.org/abs/1912.12719v1
• Date: Sun, 29 Dec 2019 20:07:18 GMT
• Title: Augmented Replay Memory in Reinforcement Learning With Continuous Control
• Authors: Mirza Ramicic, Andrea Bonarini
• Abstract summary: Online reinforcement learning agents are currently able to process an increasing amount of data by converting it into a higher order value functions. This expansion increases the agent's state space enabling it to scale up to a more complex problems but also increases the risk of forgetting by learning on redundant or conflicting data. To improve the approximation of a large amount of data, a random mini-batch of the past experiences that are stored in the replay memory buffer is often replayed at each learning step.
• Score: 1.6752182911522522
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online reinforcement learning agents are currently able to process an increasing amount of data by converting it into a higher order value functions. This expansion of the information collected from the environment increases the agent's state space enabling it to scale up to a more complex problems but also increases the risk of forgetting by learning on redundant or conflicting data. To improve the approximation of a large amount of data, a random mini-batch of the past experiences that are stored in the replay memory buffer is often replayed at each learning step. The proposed work takes inspiration from a biological mechanism which act as a protective layer of human brain higher cognitive functions: active memory consolidation mitigates the effect of forgetting of previous memories by dynamically processing the new ones. The similar dynamics are implemented by a proposed augmented memory replay AMR capable of optimizing the replay of the experiences from the agent's memory structure by altering or augmenting their relevance. Experimental results show that an evolved AMR augmentation function capable of increasing the significance of the specific memories is able to further increase the stability and convergence speed of the learning algorithms dealing with the complexity of continuous action domains.

Related papers

• Stable Hadamard Memory: Revitalizing Memory-Augmented Agents for Reinforcement Learning [64.93848182403116]
  Current deep-learning memory models struggle in reinforcement learning environments that are partially observable and long-term. We introduce the Stable Hadamard Memory, a novel memory model for reinforcement learning agents. Our approach significantly outperforms state-of-the-art memory-based methods on challenging partially observable benchmarks.
  arXiv  Detail & Related papers  (2024-10-14T03:50:17Z)
• 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)
• Adversarially Diversified Rehearsal Memory (ADRM): Mitigating Memory Overfitting Challenge in Continual Learning [0.0]
  Continual learning focuses on learning non-stationary data distribution without forgetting previous knowledge. Rehearsal-based approaches are commonly used to combat catastrophic forgetting. We introduce the Adversarially Diversified Rehearsal Memory to address the memory overfitting challenge.
  arXiv  Detail & Related papers  (2024-05-20T06:56:43Z)
• Saliency-Guided Hidden Associative Replay for Continual Learning [13.551181595881326]
  Continual Learning is a burgeoning domain in next-generation AI, focusing on training neural networks over a sequence of tasks akin to human learning. This paper presents the Saliency Guided Hidden Associative Replay for Continual Learning. This novel framework synergizes associative memory with replay-based strategies. SHARC primarily archives salient data segments via sparse memory encoding.
  arXiv  Detail & Related papers  (2023-10-06T15:54:12Z)
• A Framework for Inference Inspired by Human Memory Mechanisms [9.408704431898279]
  We propose a PMI framework that consists of perception, memory and inference components. The memory module comprises working and long-term memory, with the latter endowed with a higher-order structure to retain extensive and complex relational knowledge and experience. We apply our PMI to improve prevailing Transformers and CNN models on question-answering tasks like bAbI-20k and Sort-of-CLEVR datasets.
  arXiv  Detail & Related papers  (2023-10-01T08:12:55Z)
• Recurrent Action Transformer with Memory [39.58317527488534]
  This paper proposes a novel model architecture that incorporates a recurrent memory mechanism designed to regulate information retention. We conduct experiments on memory-intensive environments (ViZDoom-Two-Colors, T-Maze, Memory Maze, Minigrid-Memory), classic Atari games, and MuJoCo control environments. The results show that using memory can significantly improve performance in memory-intensive environments, while maintaining or improving results in classic environments.
  arXiv  Detail & Related papers  (2023-06-15T19:29:08Z)
• Two-Memory Reinforcement Learning [7.021281655855703]
  Episodic memory and reinforcement learning both have their own strengths and weaknesses. We propose a method called Two-Memory reinforcement learning agent (2M) that combines episodic memory and reinforcement learning. Our experiments demonstrate that the 2M agent is more data efficient and outperforms both pure episodic memory and pure reinforcement learning.
  arXiv  Detail & Related papers  (2023-04-20T05:39:25Z)
• Learning Human Cognitive Appraisal Through Reinforcement Memory Unit [63.83306892013521]
  We propose a memory-enhancing mechanism for recurrent neural networks that exploits the effect of human cognitive appraisal in sequential assessment tasks. We conceptualize the memory-enhancing mechanism as Reinforcement Memory Unit (RMU) that contains an appraisal state together with two positive and negative reinforcement memories.
  arXiv  Detail & Related papers  (2022-08-06T08:56:55Z)
• Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings [89.63764845984076]
  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.
  arXiv  Detail & Related papers  (2021-03-04T08:14:10Z)
• Learning to Learn Variational Semantic Memory [132.39737669936125]
  We introduce variational semantic memory into meta-learning to acquire long-term knowledge for few-shot learning. The semantic memory is grown from scratch and gradually consolidated by absorbing information from tasks it experiences. We formulate memory recall as the variational inference of a latent memory variable from addressed contents.
  arXiv  Detail & Related papers  (2020-10-20T15:05:26Z)
• Incremental Training of a Recurrent Neural Network Exploiting a Multi-Scale Dynamic Memory [79.42778415729475]
  We propose a novel incrementally trained recurrent architecture targeting explicitly multi-scale learning. We show how to extend the architecture of a simple RNN by separating its hidden state into different modules. We discuss a training algorithm where new modules are iteratively added to the model to learn progressively longer dependencies.
  arXiv  Detail & Related papers  (2020-06-29T08:35:49Z)

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.