Sam Devlin | DeepAI

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∙ 03/28/2023

Adaptive Scaffolding in Block-Based Programming via Synthesizing New Tasks as Pop Quizzes

Block-based programming environments are increasingly used to introduce ...

0 Ahana Ghosh, et al. ∙

∙ 03/02/2023

Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games

We aim to understand how people assess human likeness in navigation prod...

0 Stephanie Milani, et al. ∙

∙ 02/15/2023

Trust-Region-Free Policy Optimization for Stochastic Policies

Trust Region Policy Optimization (TRPO) is an iterative method that simu...

0 Mingfei Sun, et al. ∙

∙ 01/30/2023

Contrastive Meta-Learning for Partially Observable Few-Shot Learning

Many contrastive and meta-learning approaches learn representations by i...

19 Adam Jelley, et al. ∙

∙ 01/25/2023

Imitating Human Behaviour with Diffusion Models

Diffusion models have emerged as powerful generative models in the text-...

0 Tim Pearce, et al. ∙

∙ 11/20/2022

UniMASK: Unified Inference in Sequential Decision Problems

Randomly masking and predicting word tokens has been a successful approa...

0 Micah Carroll, et al. ∙

∙ 04/28/2022

Towards Flexible Inference in Sequential Decision Problems via Bidirectional Transformers

Randomly masking and predicting word tokens has been a successful approa...

2 Micah Carroll, et al. ∙

∙ 01/31/2022

Monotonic Improvement Guarantees under Non-stationarity for Decentralized PPO

We present a new monotonic improvement guarantee for optimizing decentra...

0 Mingfei Sun, et al. ∙

∙ 01/31/2022

You May Not Need Ratio Clipping in PPO

Proximal Policy Optimization (PPO) methods learn a policy by iteratively...

0 Mingfei Sun, et al. ∙

∙ 12/11/2021

Deterministic and Discriminative Imitation (D2-Imitation): Revisiting Adversarial Imitation for Sample Efficiency

Sample efficiency is crucial for imitation learning methods to be applic...

0 Mingfei Sun, et al. ∙

∙ 05/20/2021

Navigation Turing Test (NTT): Learning to Evaluate Human-Like Navigation

A key challenge on the path to developing agents that learn complex huma...

0 Sam Devlin, et al. ∙

∙ 01/26/2021

The MineRL 2020 Competition on Sample Efficient Reinforcement Learning using Human Priors

Although deep reinforcement learning has led to breakthroughs in many di...

11 William H. Guss, et al. ∙

∙ 01/14/2021

Evaluating the Robustness of Collaborative Agents

In order for agents trained by deep reinforcement learning to work along...

6 Paul Knott, et al. ∙

∙ 01/11/2021

Deep Interactive Bayesian Reinforcement Learning via Meta-Learning

Agents that interact with other agents often do not know a priori what t...

0 Luisa Zintgraf, et al. ∙

∙ 12/21/2020

Difference Rewards Policy Gradients

Policy gradient methods have become one of the most popular classes of a...

0 Jacopo Castellini, et al. ∙

∙ 09/01/2020

"It's Unwieldy and It Takes a Lot of Time." Challenges and Opportunities for Creating Agents in Commercial Games

Game agents such as opponents, non-player characters, and teammates are ...

15 Mikhail Jacob, et al. ∙

∙ 07/06/2020

Meta-Learning for Variational Inference

Variational inference (VI) plays an essential role in approximate Bayesi...

19 Ruqi Zhang, et al. ∙

∙ 06/15/2020

Analytic Manifold Learning: Unifying and Evaluating Representations for Continuous Control

We address the problem of learning reusable state representations from s...

7 Rika Antonova, et al. ∙

∙ 06/08/2020

A Comparison of Self-Play Algorithms Under a Generalized Framework

Throughout scientific history, overarching theoretical frameworks have a...

14 Daniel Hernández, et al. ∙

∙ 03/27/2020

Rolling Horizon Evolutionary Algorithms for General Video Game Playing

Game-playing Evolutionary Algorithms, specifically Rolling Horizon Evolu...

4 Raluca D. Gaina, et al. ∙

∙ 10/28/2019

Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck

The ability for policies to generalize to new environments is key to the...

16 Maximilian Igl, et al. ∙

∙ 03/13/2019

Resource Abstraction for Reinforcement Learning in Multiagent Congestion Problems

Real-world congestion problems (e.g. traffic congestion) are typically v...

0 Kleanthis Malialis, et al. ∙

∙ 01/23/2019

The Multi-Agent Reinforcement Learning in MalmÖ (MARLÖ) Competition

Learning in multi-agent scenarios is a fruitful research direction, but ...

12 Diego Pérez-Liébana, et al. ∙

∙ 08/03/2018

The Text-Based Adventure AI Competition

In 2016 and 2017 at the IEEE Conference on Computational Intelligence in...

0 Timothy Atkinson, et al. ∙

∙ 11/17/2017

Win Prediction in Esports: Mixed-Rank Match Prediction in Multi-player Online Battle Arena Games

Esports has emerged as a popular genre for players as well as spectators...

0 Victoria Hodge, et al. ∙

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