11 citations · 34 across the 9 of their papers we have counts for
9 papers
Neural Network Training on In-memory-computing Hardware with Radix-4 Gradients
Christopher Grimm, Naveen Verma
Deep learning training involves a large number of operations, which are dominated by high dimensionality Matrix-Vector Multiplies (MVMs). This has motivated hardware accelerators t…
Proper Value Equivalence
Christopher Grimm, André Barreto, Gregory Farquhar +2
One of the main challenges in model-based reinforcement learning (RL) is to decide which aspects of the environment should be modeled. The value-equivalence (VE) principle proposes…
The Value Equivalence Principle for Model-Based Reinforcement Learning
Christopher Grimm, André Barreto, Satinder Singh +1
Learning models of the environment from data is often viewed as an essential component to building intelligent reinforcement learning (RL) agents. The common practice is to separat…
Disentangled Cumulants Help Successor Representations Transfer to New Tasks
Christopher Grimm, Irina Higgins, Andre Barreto +5
Biological intelligence can learn to solve many diverse tasks in a data efficient manner by re-using basic knowledge and skills from one task to another. Furthermore, many of such…
Learning Independently-Obtainable Reward Functions
Christopher Grimm, Satinder Singh
We present a novel method for learning a set of disentangled reward functions that sum to the original environment reward and are constrained to be independently obtainable. We def…
Mitigating Planner Overfitting in Model-Based Reinforcement Learning
Dilip Arumugam, David Abel, Kavosh Asadi +5
An agent with an inaccurate model of its environment faces a difficult choice: it can ignore the errors in its model and act in the real world in whatever way it determines is opti…