Papers Meta Reinforcement Learning
“Meta Reinforcement Learning” 태그가 달린 논문 278편 · 필터 해제
Meta-Reinforcement Learning for Fast and Data-Efficient Spectrum Allocation in Dynamic Wireless Networks
The dynamic allocation of spectrum in 5G / 6G networks is critical to efficient resource utilization. However, applying traditional deep reinforcement learning (DRL) is often infeasible due to its immense sample complexi…
Deep Reinforcement LearningFairnessMeta-LearningMeta Reinforcement LearningLearning Task Belief Similarity with Latent Dynamics for Meta-Reinforcement Learning
Meta-reinforcement learning requires utilizing prior task distribution information obtained during exploration to rapidly adapt to unknown tasks. The efficiency of an agent's exploration hinges on accurately identifying …
Meta Reinforcement LearningMuJoCoScaling Algorithm Distillation for Continuous Control with Mamba
Algorithm Distillation (AD) was recently proposed as a new approach to perform In-Context Reinforcement Learning (ICRL) by modeling across-episodic training histories autoregressively with a causal transformer model. How…
continuous-controlContinuous ControlIn-Context Reinforcement LearningMamba+3Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning
We introduce Unsupervised Meta-Testing with Conditional Neural Processes (UMCNP), a novel hybrid few-shot meta-reinforcement learning (meta-RL) method that uniquely combines, yet distinctly separates, parameterized polic…
continuous-controlContinuous ControlMeta Reinforcement LearningBayesian Meta-Reinforcement Learning with Laplace Variational Recurrent Networks
Meta-reinforcement learning trains a single reinforcement learning agent on a distribution of tasks to quickly generalize to new tasks outside of the training set at test time. From a Bayesian perspective, one can interp…
Meta Reinforcement Learningreinforcement-learningReinforcement LearningVariational InferenceMeta-reinforcement learning with minimum attention
Minimum attention applies the least action principle in the changes of control concerning state and time, first proposed by Brockett. The involved regularization is highly relevant in emulating biological control, such a…
Meta-LearningMeta Reinforcement Learningreinforcement-learningReinforcement Learning+1Meta-World+: An Improved, Standardized, RL Benchmark
Meta-World is widely used for evaluating multi-task and meta-reinforcement learning agents, which are challenged to master diverse skills simultaneously. Since its introduction however, there have been numerous undocumen…
Meta Reinforcement Learningreinforcement-learningReinforcement LearningFast and Robust: Task Sampling with Posterior and Diversity Synergies for Adaptive Decision-Makers in Randomized Environments
Task robust adaptation is a long-standing pursuit in sequential decision-making. Some risk-averse strategies, e.g., the conditional value-at-risk principle, are incorporated in domain randomization or meta reinforcement …
Decision MakingDiversityMeta Reinforcement LearningSequential Decision MakingInstructRAG: Leveraging Retrieval-Augmented Generation on Instruction Graphs for LLM-Based Task Planning
Recent advancements in large language models (LLMs) have enabled their use as agents for planning complex tasks. Existing methods typically rely on a thought-action-observation (TAO) process to enhance LLM performance, b…
Meta-LearningMeta Reinforcement LearningRAGreinforcement-learning+4Embodied World Models Emerge from Navigational Task in Open-Ended Environments
Spatial reasoning in partially observable environments has often been approached through passive predictive models, yet theories of embodied cognition suggest that genuinely useful representations arise only when percept…
Meta Reinforcement LearningSpatial ReasoningUAS Visual Navigation in Large and Unseen Environments via a Meta Agent
The aim of this work is to develop an approach that enables Unmanned Aerial System (UAS) to efficiently learn to navigate in large-scale urban environments and transfer their acquired expertise to novel environments. To …
Incremental LearningMeta Reinforcement LearningNavigatePhilosophy+4Meta-Reinforcement Learning with Discrete World Models for Adaptive Load Balancing
We integrate a meta-reinforcement learning algorithm with the DreamerV3 architecture to improve load balancing in operating systems. This approach enables rapid adaptation to dynamic workloads with minimal retraining, ou…
ManagementMeta Reinforcement Learningreinforcement-learningReinforcement LearningOptimizing Test-Time Compute via Meta Reinforcement Fine-Tuning
Training models to effectively use test-time compute is crucial for improving the reasoning performance of LLMs. Current methods mostly do so via fine-tuning on search traces or running RL with 0/1 outcome reward, but do…
MathMeta Reinforcement LearningReinforcement Learning (RL)Teleology-Driven Affective Computing: A Causal Framework for Sustained Well-Being
Affective computing has made significant strides in emotion recognition and generation, yet current approaches mainly focus on short-term pattern recognition and lack a comprehensive framework to guide affective agents t…
Emotion RecognitionMeta Reinforcement LearningPRISM: A Robust Framework for Skill-based Meta-Reinforcement Learning with Noisy Demonstrations
Meta-reinforcement learning (Meta-RL) facilitates rapid adaptation to unseen tasks but faces challenges in long-horizon environments. Skill-based approaches tackle this by decomposing state-action sequences into reusable…
Decision MakingMeta Reinforcement LearningTask-Aware Virtual Training: Enhancing Generalization in Meta-Reinforcement Learning for Out-of-Distribution Tasks
Meta reinforcement learning aims to develop policies that generalize to unseen tasks sampled from a task distribution. While context-based meta-RL methods improve task representation using task latents, they often strugg…
Meta Reinforcement LearningMuJoCoRepresentation LearningCoreset-Based Task Selection for Sample-Efficient Meta-Reinforcement Learning
We study task selection to enhance sample efficiency in model-agnostic meta-reinforcement learning (MAML-RL). Traditional meta-RL typically assumes that all available tasks are equally important, which can lead to task r…
Meta Reinforcement LearningToward Task Generalization via Memory Augmentation in Meta-Reinforcement Learning
Agents trained via reinforcement learning (RL) often struggle to perform well on tasks that differ from those encountered during training. This limitation presents a challenge to the broader deployment of RL in diverse a…
Meta Reinforcement Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)+1TIMRL: A Novel Meta-Reinforcement Learning Framework for Non-Stationary and Multi-Task Environments
In recent years, meta-reinforcement learning (meta-RL) algorithm has been proposed to improve sample efficiency in the field of decision-making and control, enabling agents to learn new knowledge from a small number of s…
Decision MakingMeta Reinforcement LearningMuJoCoreinforcement-learning+1Hierarchical Multi-agent Meta-Reinforcement Learning for Cross-channel Bidding
Real-time bidding (RTB) plays a pivotal role in online advertising ecosystems. Advertisers employ strategic bidding to optimize their advertising impact while adhering to various financial constraints, such as the return…
global-optimizationMeta Reinforcement LearningMulti-agent Reinforcement Learningreinforcement-learning+1