The following posters will be presented in this session:

(A2) Open-Ended Learning Strategies for Learning Complex Locomotion Skills
(A3) Neural Processes with Stochastic Attention: Paying more attention to the context dataset
(B0) Transformers Can Do Bayesian-Inference By Meta-Learning on Prior-Data
(B1) On the Practical Consistency of Meta-Reinforcement Learning Algorithms
(C3) Transfer Learning for Bayesian HPO with End-to-End Landmark Meta-Features
(F1) Introducing Symmetries to Black Box Meta Reinforcement Learning
(F3) Task Attended Meta-Learning for Few-Shot Learning
(G0) One Step at a Time: Pros and Cons of Multi-Step Meta-Gradient Reinforcement Learning
(G1) Bootstrapped Meta-Learning
(G2) Skill-based Meta-Reinforcement Learning
(H2) On the Role of Pre-training for Meta Few-Shot Learning