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Methods/Experience Replay

Experience Replay

Reinforcement LearningIntroduced 1993865 papers

Description

Experience Replay is a replay memory technique used in reinforcement learning where we store the agent’s experiences at each time-step, $e\_{t} = \left(s\_{t}, a\_{t}, r\_{t}, s\_{t+1}\right)$ in a data-set $D = e\_{1}, \cdots, e\_{N}$ , pooled over many episodes into a replay memory. We then usually sample the memory randomly for a minibatch of experience, and use this to learn off-policy, as with Deep Q-Networks. This tackles the problem of autocorrelation leading to unstable training, by making the problem more like a supervised learning problem.

Image Credit: Hands-On Reinforcement Learning with Python, Sudharsan Ravichandiran

Papers Using This Method

Turning Sand to Gold: Recycling Data to Bridge On-Policy and Off-Policy Learning via Causal Bound2025-07-15 Deep Reinforcement Learning with Gradient Eligibility Traces2025-07-12 Multi-Objective Reinforcement Learning for Cognitive Radar Resource Management2025-06-25 CAWR: Corruption-Averse Advantage-Weighted Regression for Robust Policy Optimization2025-06-18 Reliable Critics: Monotonic Improvement and Convergence Guarantees for Reinforcement Learning2025-06-08 Contextual Experience Replay for Self-Improvement of Language Agents2025-06-07 Gradual Transition from Bellman Optimality Operator to Bellman Operator in Online Reinforcement Learning2025-06-06 A Novel Deep Reinforcement Learning Method for Computation Offloading in Multi-User Mobile Edge Computing with Decentralization2025-06-03 FastTD3: Simple, Fast, and Capable Reinforcement Learning for Humanoid Control2025-05-28 Analyzing Mitigation Strategies for Catastrophic Forgetting in End-to-End Training of Spoken Language Models2025-05-23 LLM-Explorer: A Plug-in Reinforcement Learning Policy Exploration Enhancement Driven by Large Language Models2025-05-21 LifelongAgentBench: Evaluating LLM Agents as Lifelong Learners2025-05-17 Unveiling the Black Box: A Multi-Layer Framework for Explaining Reinforcement Learning-Based Cyber Agents2025-05-16 Electric Bus Charging Schedules Relying on Real Data-Driven Targets Based on Hierarchical Deep Reinforcement Learning2025-05-15 GradMix: Gradient-based Selective Mixup for Robust Data Augmentation in Class-Incremental Learning2025-05-13 Monte Carlo Beam Search for Actor-Critic Reinforcement Learning in Continuous Control2025-05-13 Deep reinforcement learning-based longitudinal control strategy for automated vehicles at signalised intersections2025-05-13 Online Learning-based Adaptive Beam Switching for 6G Networks: Enhancing Efficiency and Resilience2025-05-12 Energy Efficient RSMA-Based LEO Satellite Communications Assisted by UAV-Mounted BD-Active RIS: A DRL Approach2025-05-07 A Goal-Oriented Reinforcement Learning-Based Path Planning Algorithm for Modular Self-Reconfigurable Satellites2025-05-04