Meta Reinforcement Learning
88 papers with code • 2 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Meta Reinforcement Learning
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
Libraries
Use these libraries to find Meta Reinforcement Learning models and implementations
Meta-World Benchmark
Latest papers with no code
SplAgger: Split Aggregation for Meta-Reinforcement Learning
However, it remains unclear whether task inference sequence models are beneficial even when task inference objectives are not.
DynaMITE-RL: A Dynamic Model for Improved Temporal Meta-Reinforcement Learning
We introduce DynaMITE-RL, a meta-reinforcement learning (meta-RL) approach to approximate inference in environments where the latent state evolves at varying rates.
Hierarchical Transformers are Efficient Meta-Reinforcement Learners
We introduce Hierarchical Transformers for Meta-Reinforcement Learning (HTrMRL), a powerful online meta-reinforcement learning approach.
Analysing the Sample Complexity of Opponent Shaping
Providing theoretical guarantees for M-FOS is hard because A) there is little literature on theoretical sample complexity bounds for meta-reinforcement learning B) M-FOS operates in continuous state and action spaces, so theoretical analysis is challenging.
In-context learning agents are asymmetric belief updaters
We study the in-context learning dynamics of large language models (LLMs) using three instrumental learning tasks adapted from cognitive psychology.
Learning to Abstract Visuomotor Mappings using Meta-Reinforcement Learning
We investigated the human capacity to acquire multiple visuomotor mappings for de novo skills.
Towards an Information Theoretic Framework of Context-Based Offline Meta-Reinforcement Learning
As a marriage between offline RL and meta-RL, the advent of offline meta-reinforcement learning (OMRL) has shown great promise in enabling RL agents to multi-task and quickly adapt while acquiring knowledge safely.
Meta Reinforcement Learning for Strategic IoT Deployments Coverage in Disaster-Response UAV Swarms
Our simulation results prove that our introduced approach is better than the three state-of-the-art algorithms in providing coverage to strategic locations with fast convergence.
Towards an Adaptable and Generalizable Optimization Engine in Decision and Control: A Meta Reinforcement Learning Approach
Many machine learning-based works proposed to improve MPC by a) learning or fine-tuning the dynamics/ cost function, or b) learning to optimize for the update of the MPC controllers.
Meta Reinforcement Learning for Multi-Task Offloading in Vehicular Edge Computing
The objective is to design a unified solution to minimize task execution time under different MTO scenarios.
