Maximilian Igl | DeepAI

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∙ 12/14/2022

Particle-Based Score Estimation for State Space Model Learning in Autonomous Driving

Multi-object state estimation is a fundamental problem for robotic appli...

2 Angad Singh, et al. ∙

∙ 05/06/2022

Symphony: Learning Realistic and Diverse Agents for Autonomous Driving Simulation

Simulation is a crucial tool for accelerating the development of autonom...

8 Maximilian Igl, et al. ∙

∙ 07/17/2021

Implicit Communication as Minimum Entropy Coupling

In many common-payoff games, achieving good performance requires players...

3 Samuel Sokota, et al. ∙

∙ 03/01/2021

Snowflake: Scaling GNNs to High-Dimensional Continuous Control via Parameter Freezing

Recent research has shown that Graph Neural Networks (GNNs) can learn po...

8 Charlie Blake, et al. ∙

∙ 10/05/2020

My Body is a Cage: the Role of Morphology in Graph-Based Incompatible Control

Multitask Reinforcement Learning is a promising way to obtain models wit...

7 Vitaly Kurin, et al. ∙

∙ 10/02/2020

Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning

Meta-learning is a powerful tool for learning policies that can adapt ef...

4 Luisa Zintgraf, et al. ∙

∙ 06/10/2020

The Impact of Non-stationarity on Generalisation in Deep Reinforcement Learning

Non-stationarity arises in Reinforcement Learning (RL) even in stationar...

13 Maximilian Igl, 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. ∙

∙ 10/18/2019

VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning

Trading off exploration and exploitation in an unknown environment is ke...

66 Luisa Zintgraf, et al. ∙

∙ 04/01/2019

Multitask Soft Option Learning

We present Multitask Soft Option Learning (MSOL), a hierarchical multita...

98 Maximilian Igl, et al. ∙

∙ 06/06/2018

Deep Variational Reinforcement Learning for POMDPs

Many real-world sequential decision making problems are partially observ...

2 Maximilian Igl, et al. ∙

∙ 02/13/2018

Tighter Variational Bounds are Not Necessarily Better

We provide theoretical and empirical evidence that using tighter evidenc...

0 Tom Rainforth, et al. ∙

∙ 10/31/2017

TreeQN and ATreeC: Differentiable Tree Planning for Deep Reinforcement Learning

Combining deep model-free reinforcement learning with on-line planning i...

0 Gregory Farquhar, et al. ∙

∙ 05/29/2017

Auto-Encoding Sequential Monte Carlo

We introduce AESMC: a method for using deep neural networks for simultan...

0 Tuan Anh Le, et al. ∙

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