6 citations · 12 across the 14 of their papers we have counts for
14 papers · 1 filter
Scalable Meta-Learning via Mixed-Mode Differentiation
Iurii Kemaev, Dan A Calian, Luisa M Zintgraf +2
Gradient-based bilevel optimisation is a powerful technique with applications in hyperparameter optimisation, task adaptation, algorithm discovery, meta-learning more broadly, and…
Wasserstein Policy Optimization
David Pfau, Ian Davies, Diana Borsa +3
We introduce Wasserstein Policy Optimization (WPO), an actor-critic algorithm for reinforcement learning in continuous action spaces. WPO can be derived as an approximation to Wass…
General Uncertainty Estimation with Delta Variances
Simon Schmitt, John Shawe-Taylor, Hado van Hasselt
Decision makers may suffer from uncertainty induced by limited data. This may be mitigated by accounting for epistemic uncertainty, which is however challenging to estimate efficie…
On the Convergence of Bounded Agents
David Abel, André Barreto, Hado van Hasselt +3
When has an agent converged? Standard models of the reinforcement learning problem give rise to a straightforward definition of convergence: An agent converges when its behavior or…
Learning How to Infer Partial MDPs for In-Context Adaptation and Exploration
Chentian Jiang, Nan Rosemary Ke, Hado van Hasselt
To generalize across tasks, an agent should acquire knowledge from past tasks that facilitate adaptation and exploration in future tasks. We focus on the problem of in-context adap…
Optimistic Meta-Gradients
Sebastian Flennerhag, Tom Zahavy, Brendan O'Donoghue +3
We study the connection between gradient-based meta-learning and convex op-timisation. We observe that gradient descent with momentum is a special case of meta-gradients, and build…
Human-level Atari 200x faster
Steven Kapturowski, Víctor Campos, Ray Jiang +4
The task of building general agents that perform well over a wide range of tasks has been an important goal in reinforcement learning since its inception. The problem has been subj…
Selective Credit Assignment
Veronica Chelu, Diana Borsa, Doina Precup +1
Efficient credit assignment is essential for reinforcement learning algorithms in both prediction and control settings. We describe a unified view on temporal-difference algorithms…
Chaining Value Functions for Off-Policy Learning
Simon Schmitt, John Shawe-Taylor, Hado van Hasselt
To accumulate knowledge and improve its policy of behaviour, a reinforcement learning agent can learn `off-policy' about policies that differ from the policy used to generate its e…
Self-Consistent Models and Values
Gregory Farquhar, Kate Baumli, Zita Marinho +4
Learned models of the environment provide reinforcement learning (RL) agents with flexible ways of making predictions about the environment. In particular, models enable planning,…
Introducing Symmetries to Black Box Meta Reinforcement Learning
Louis Kirsch, Sebastian Flennerhag, Hado van Hasselt +3
Meta reinforcement learning (RL) attempts to discover new RL algorithms automatically from environment interaction. In so-called black-box approaches, the policy and the learning a…
Bootstrapped Meta-Learning
Sebastian Flennerhag, Yannick Schroecker, Tom Zahavy +3
Meta-learning empowers artificial intelligence to increase its efficiency by learning how to learn. Unlocking this potential involves overcoming a challenging meta-optimisation pro…
Learning Expected Emphatic Traces for Deep RL
Ray Jiang, Shangtong Zhang, Veronica Chelu +2
Off-policy sampling and experience replay are key for improving sample efficiency and scaling model-free temporal difference learning methods. When combined with function approxima…
Emphatic Algorithms for Deep Reinforcement Learning
Ray Jiang, Tom Zahavy, Zhongwen Xu +4
Off-policy learning allows us to learn about possible policies of behavior from experience generated by a different behavior policy. Temporal difference (TD) learning algorithms ca…