963 citations · 1.3k across the 8 of their papers we have counts for
9 papers · 1 filter
Finding online neural update rules by learning to remember
Karol Gregor
We investigate learning of the online local update rules for neural activations (bodies) and weights (synapses) from scratch. We represent the states of each weight and activation…
Causally Correct Partial Models for Reinforcement Learning
Danilo J. Rezende, Ivo Danihelka, George Papamakarios +11
In reinforcement learning, we can learn a model of future observations and rewards, and use it to plan the agent's next actions. However, jointly modeling future observations can b…
Shaping Belief States with Generative Environment Models for RL
Karol Gregor, Danilo Jimenez Rezende, Frederic Besse +3
When agents interact with a complex environment, they must form and maintain beliefs about the relevant aspects of that environment. We propose a way to efficiently train expressiv…
An investigation of model-free planning
Arthur Guez, Mehdi Mirza, Karol Gregor +10
The field of reinforcement learning (RL) is facing increasingly challenging domains with combinatorial complexity. For an RL agent to address these challenges, it is essential that…
Learning Attractor Dynamics for Generative Memory
Yan Wu, Greg Wayne, Karol Gregor +1
A central challenge faced by memory systems is the robust retrieval of a stored pattern in the presence of interference due to other stored patterns and noise. A theoretically well…
Temporal Difference Variational Auto-Encoder
Karol Gregor, George Papamakarios, Frederic Besse +2
To act and plan in complex environments, we posit that agents should have a mental simulator of the world with three characteristics: (a) it should build an abstract state represen…