REVIEW 5 cited by
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
Trading off exploration and exploitation in an unknown environment is key to maximising expected return during learning. A Bayes-optimal policy, which does so optimally, conditions its actions not only on the environment state but on the agent's uncertainty about the environment. Computing a Bayes-optimal policy is however intractable for all but the smallest tasks. In this paper, we introduce variational Bayes-Adaptive Deep RL (variBAD), a way to meta-learn to perform approximate inference in an unknown environment, and incorporate task uncertainty directly during action selection. In a grid-world domain, we illustrate how variBAD performs structured online exploration as a function of task uncertainty. We further evaluate variBAD on MuJoCo domains widely used in meta-RL and show that it achieves higher online return than existing methods.
Forward citations
Cited by 5 Pith papers
-
In-Context World Modeling for Robotic Control
Prepending a few self-generated random interaction clips as context lets VLA policies identify novel camera viewpoints and morphologies at test time and outperform multi-view baselines without parameter updates.
-
Behavioral Exploration: Learning to Explore via In-Context Adaptation
A coverage-conditioned behavioral cloning policy adapts in-context to its own history, making robots explore new expert-like behaviors online without online reinforcement learning.
-
Uncertainty Prioritized Experience Replay
UPER uses ensemble-based epistemic and aleatoric uncertainty to compute an information gain priority for experience replay, outperforming TD-error prioritization on Atari-57.
-
Unsupervised Meta-Testing with Conditional Neural Processes for Hybrid Meta-Reinforcement Learning
A hybrid meta-RL method uses offline-trained conditional neural processes to generate extra rollouts, enabling reward-free adaptation to an unseen task from a single real rollout.
-
Reflect-then-Plan: Offline Model-Based Planning through a Doubly Bayesian Lens
An offline RL policy can be improved at test time by inferring a latent belief over environment dynamics from past transitions and planning with model-based rollouts averaged over that belief.
Discussion (0). Continue with ORCID to comment.