183 citations · 474 across the 18 of their papers we have counts for
18 papers
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…
Deep Reinforcement Learning with Plasticity Injection
Evgenii Nikishin, Junhyuk Oh, Georg Ostrovski +4
A growing body of evidence suggests that neural networks employed in deep reinforcement learning (RL) gradually lose their plasticity, the ability to learn from new data; however,…
Generalised Policy Improvement with Geometric Policy Composition
Shantanu Thakoor, Mark Rowland, Diana Borsa +3
We introduce a method for policy improvement that interpolates between the greedy approach of value-based reinforcement learning (RL) and the full planning approach typical of mode…
The Phenomenon of Policy Churn
Tom Schaul, André Barreto, John Quan +1
We identify and study the phenomenon of policy churn, that is, the rapid change of the greedy policy in value-based reinforcement learning. Policy churn operates at a surprisingly…
Model-Value Inconsistency as a Signal for Epistemic Uncertainty
Angelos Filos, Eszter Vértes, Zita Marinho +7
Using a model of the environment and a value function, an agent can construct many estimates of a state's value, by unrolling the model for different lengths and bootstrapping with…
Temporal Abstraction in Reinforcement Learning with the Successor Representation
Marlos C. Machado, Andre Barreto, Doina Precup +1
Reasoning at multiple levels of temporal abstraction is one of the key attributes of intelligence. In reinforcement learning, this is often modeled through temporally extended cour…