PcLast: Discovering Plannable Continuous Latent States

no code implementations • 6 Nov 2023 • Anurag Koul, Shivakanth Sujit, Shaoru Chen, Ben Evans, Lili Wu, Byron Xu, Rajan Chari, Riashat Islam, Raihan Seraj, Yonathan Efroni, Lekan Molu, Miro Dudik, John Langford, Alex Lamb

Goal-conditioned planning benefits from learned low-dimensional representations of rich, high-dimensional observations.

Offline Policy Comparison with Confidence: Benchmarks and Baselines

2 code implementations • 22 May 2022 • Anurag Koul, Mariano Phielipp, Alan Fern

Decision makers often wish to use offline historical data to compare sequential-action policies at various world states.

Dream and Search to Control: Latent Space Planning for Continuous Control

1 code implementation • 19 Oct 2020 • Anurag Koul, Varun V. Kumar, Alan Fern, Somdeb Majumdar

Learning and planning with latent space dynamics has been shown to be useful for sample efficiency in model-based reinforcement learning (MBRL) for discrete and continuous control tasks.

Continuous Control Model-based Reinforcement Learning +1

Re-understanding Finite-State Representations of Recurrent Policy Networks

1 code implementation • 6 Jun 2020 • Mohamad H. Danesh, Anurag Koul, Alan Fern, Saeed Khorram

We introduce an approach for understanding control policies represented as recurrent neural networks.

Atari Games

Learning Finite State Representations of Recurrent Policy Networks

no code implementations • ICLR 2019 • Anurag Koul, Sam Greydanus, Alan Fern

Recurrent neural networks (RNNs) are an effective representation of control policies for a wide range of reinforcement and imitation learning problems.

Atari Games Imitation Learning

Visualizing and Understanding Atari Agents

3 code implementations • ICML 2018 • Sam Greydanus, Anurag Koul, Jonathan Dodge, Alan Fern

While deep reinforcement learning (deep RL) agents are effective at maximizing rewards, it is often unclear what strategies they use to do so.

Reinforcement Learning (RL)