Understanding the effect of varying amounts of replay per step
Model-based reinforcement learning uses models to plan, where the predictions and policies of an agent can be improved by using more computation without additional data from the environment, thereby improving sample efficiency. However, learning accurate estimates of the model is hard. Subsequently, the natural question is whether we can get similar benefits as planning with model-free methods. Experience replay is an essential component of many model-free algorithms enabling sample-efficient learning and stability by providing a mechanism to store past experiences for further reuse in the gradient computational process. Prior works have established connections between models and experience replay by planning with the latter. This involves increasing the number of times a mini-batch is sampled and used for updates at each step (amount of replay per step). We attempt to exploit this connection by doing a systematic study on the effect of varying amounts of replay per step in a well-known model-free algorithm: Deep Q-Network (DQN) in the Mountain Car environment. We empirically show that increasing replay improves DQN's sample efficiency, reduces the variation in its performance, and makes it more robust to change in hyperparameters. Altogether, this takes a step toward a better algorithm for deployment.
Code (0)
등록된 구현이 없습니다.
Tasks
Model-based Reinforcement LearningMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
FOREVER: Forgetting Curve-Inspired Memory Replay for Language Model Continual Learning
Continual learning (CL) for large language models (LLMs) aims to enable sequential knowledge acquisition without catastrophic forgetting. Memory replay methods are widely used for their practicality and effectiveness, bu…
Continual LearningSelective Timestep Weighting and Advantage-Based Replay for Sample-Efficient Diffusion RLHF
Reinforcement learning from human feedback (RLHF) has emerged as a powerful paradigm for aligning generative models with human preferences. However, applying RLHF to diffusion models remains highly feedback inefficient, …
Reinforcement LearningRevisiting Fundamentals of Experience Replay
Experience replay is central to off-policy algorithms in deep reinforcement learning (RL), but there remain significant gaps in our understanding. We therefore present a systematic and extensive analysis of experience re…
Deep Reinforcement LearningDQN Replay DatasetQ-LearningReinforcement Learning (RL)Generative appearance replay for continual unsupervised domain adaptation
Deep learning models can achieve high accuracy when trained on large amounts of labeled data. However, real-world scenarios often involve several challenges: Training data may become available in installments, may origin…
Continual LearningDomain AdaptationUnsupervised Domain AdaptationProgressive Latent Replay for efficient Generative Rehearsal
We introduce a new method for internal replay that modulates the frequency of rehearsal based on the depth of the network. While replay strategies mitigate the effects of catastrophic forgetting in neural networks, recen…
Continual Learning