252 citations · 1k across the 12 of their papers we have counts for
11 papers · 1 filter
Open-Ended Learning Leads to Generally Capable Agents
Open Ended Learning Team, Adam Stooke, Anuj Mahajan +15
In this work we create agents that can perform well beyond a single, individual task, that exhibit much wider generalisation of behaviour to a massive, rich space of challenges. We…
Real World Games Look Like Spinning Tops
Wojciech Marian Czarnecki, Gauthier Gidel, Brendan Tracey +4
This paper investigates the geometrical properties of real world games (e.g. Tic-Tac-Toe, Go, StarCraft II). We hypothesise that their geometrical structure resemble a spinning top…
A Deep Neural Network's Loss Surface Contains Every Low-dimensional Pattern
Wojciech Marian Czarnecki, Simon Osindero, Razvan Pascanu +1
The work "Loss Landscape Sightseeing with Multi-Point Optimization" (Skorokhodov and Burtsev, 2019) demonstrated that one can empirically find arbitrary 2D binary patterns inside l…
Stabilizing Transformers for Reinforcement Learning
Emilio Parisotto, H. Francis Song, Jack W. Rae +10
Owing to their ability to both effectively integrate information over long time horizons and scale to massive amounts of data, self-attention architectures have recently shown brea…
Distilling Policy Distillation
Wojciech Marian Czarnecki, Razvan Pascanu, Simon Osindero +3
The transfer of knowledge from one policy to another is an important tool in Deep Reinforcement Learning. This process, referred to as distillation, has been used to great success,…
Open-ended Learning in Symmetric Zero-sum Games
David Balduzzi, Marta Garnelo, Yoram Bachrach +4
Zero-sum games such as chess and poker are, abstractly, functions that evaluate pairs of agents, for example labeling them `winner' and `loser'. If the game is approximately transi…