Search Results for author: Maximilian Igl

Found 15 papers, 11 papers with code

Hierarchical Imitation Learning for Stochastic Environments

no code implementations25 Sep 2023 Maximilian Igl, Punit Shah, Paul Mougin, Sirish Srinivasan, Tarun Gupta, Brandyn White, Kyriacos Shiarlis, Shimon Whiteson

However, such methods are often inappropriate for stochastic environments where the agent must also react to external factors: because agent types are inferred from the observed future trajectory during training, these environments require that the contributions of internal and external factors to the agent behaviour are disentangled and only internal factors, i. e., those under the agent's control, are encoded in the type.

Autonomous Vehicles Imitation Learning

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

no code implementations14 Dec 2022 Angad Singh, Omar Makhlouf, Maximilian Igl, Joao Messias, Arnaud Doucet, Shimon Whiteson

Recent methods addressing this problem typically differentiate through time in a particle filter, which requires workarounds to the non-differentiable resampling step, that yield biased or high variance gradient estimates.

Autonomous Driving

Communicating via Markov Decision Processes

1 code implementation17 Jul 2021 Samuel Sokota, Christian Schroeder de Witt, Maximilian Igl, Luisa Zintgraf, Philip Torr, Martin Strohmeier, J. Zico Kolter, Shimon Whiteson, Jakob Foerster

We contribute a theoretically grounded approach to MCGs based on maximum entropy reinforcement learning and minimum entropy coupling that we call MEME.

Multi-agent Reinforcement Learning

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

1 code implementation NeurIPS 2021 Charlie Blake, Vitaly Kurin, Maximilian Igl, Shimon Whiteson

Recent research has shown that graph neural networks (GNNs) can learn policies for locomotion control that are as effective as a typical multi-layer perceptron (MLP), with superior transfer and multi-task performance (Wang et al., 2018; Huang et al., 2020).

Continuous Control Vocal Bursts Intensity Prediction

Exploration in Approximate Hyper-State Space for Meta Reinforcement Learning

1 code implementation2 Oct 2020 Luisa Zintgraf, Leo Feng, Cong Lu, Maximilian Igl, Kristian Hartikainen, Katja Hofmann, Shimon Whiteson

To rapidly learn a new task, it is often essential for agents to explore efficiently -- especially when performance matters from the first timestep.

Meta-Learning Meta Reinforcement Learning +2

Multitask Soft Option Learning

1 code implementation1 Apr 2019 Maximilian Igl, Andrew Gambardella, Jinke He, Nantas Nardelli, N. Siddharth, Wendelin Böhmer, Shimon Whiteson

We present Multitask Soft Option Learning(MSOL), a hierarchical multitask framework based on Planning as Inference.

Transfer Learning

Deep Variational Reinforcement Learning for POMDPs

1 code implementation ICML 2018 Maximilian Igl, Luisa Zintgraf, Tuan Anh Le, Frank Wood, Shimon Whiteson

Many real-world sequential decision making problems are partially observable by nature, and the environment model is typically unknown.

Decision Making Inductive Bias +2

Tighter Variational Bounds are Not Necessarily Better

3 code implementations ICML 2018 Tom Rainforth, Adam R. Kosiorek, Tuan Anh Le, Chris J. Maddison, Maximilian Igl, Frank Wood, Yee Whye Teh

We provide theoretical and empirical evidence that using tighter evidence lower bounds (ELBOs) can be detrimental to the process of learning an inference network by reducing the signal-to-noise ratio of the gradient estimator.

TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning

1 code implementation ICLR 2018 Gregory Farquhar, Tim Rocktäschel, Maximilian Igl, Shimon Whiteson

To address these challenges, we propose TreeQN, a differentiable, recursive, tree-structured model that serves as a drop-in replacement for any value function network in deep RL with discrete actions.

Atari Games reinforcement-learning +2

Auto-Encoding Sequential Monte Carlo

1 code implementation ICLR 2018 Tuan Anh Le, Maximilian Igl, Tom Rainforth, Tom Jin, Frank Wood

We build on auto-encoding sequential Monte Carlo (AESMC): a method for model and proposal learning based on maximizing the lower bound to the log marginal likelihood in a broad family of structured probabilistic models.

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