23 citations · 82 across the 22 of their papers we have counts for
14 papers · 1 filter
Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models
Andrea Tirinzoni, Ahmed Touati, Jesse Farebrother +5
Unsupervised reinforcement learning (RL) aims at pre-training agents that can solve a wide range of downstream tasks in complex environments. Despite recent advancements, existing…
Fast Adaptation with Behavioral Foundation Models
Harshit Sikchi, Andrea Tirinzoni, Ahmed Touati +6
Unsupervised zero-shot reinforcement learning (RL) has emerged as a powerful paradigm for pretraining behavioral foundation models (BFMs), enabling agents to solve a wide range of…
Diffusion for World Modeling: Visual Details Matter in Atari
Eloi Alonso, Adam Jelley, Vincent Micheli +4
World models constitute a promising approach for training reinforcement learning agents in a safe and sample-efficient manner. Recent world models predominantly operate on sequence…
Visual Encoders for Data-Efficient Imitation Learning in Modern Video Games
Lukas Schäfer, Logan Jones, Anssi Kanervisto +7
Video games have served as useful benchmarks for the decision-making community, but going beyond Atari games towards modern games has been prohibitively expensive for the vast majo…
A2C is a special case of PPO
Shengyi Huang, Anssi Kanervisto, Antonin Raffin +3
Advantage Actor-critic (A2C) and Proximal Policy Optimization (PPO) are popular deep reinforcement learning algorithms used for game AI in recent years. A common understanding is t…
Insights From the NeurIPS 2021 NetHack Challenge
Eric Hambro, Sharada Mohanty, Dmitrii Babaev +26
In this report, we summarize the takeaways from the first NeurIPS 2021 NetHack Challenge. Participants were tasked with developing a program or agent that can win (i.e., 'ascend' i…