4 papers
Calibrated Value-Aware Model Learning with Probabilistic Environment Models
Claas Voelcker, Anastasiia Pedan, Arash Ahmadian +3
The idea of value-aware model learning, that models should produce accurate value estimates, has gained prominence in model-based reinforcement learning. The MuZero loss, which pen…
A Truncated Newton Method for Optimal Transport
Mete Kemertas, Amir-massoud Farahmand, Allan D. Jepson
Developing a contemporary optimal transport (OT) solver requires navigating trade-offs among several critical requirements: GPU parallelization, scalability to high-dimensional pro…
PID Accelerated Temporal Difference Algorithms
Mark Bedaywi, Amin Rakhsha, Amir-massoud Farahmand
Long-horizon tasks, which have a large discount factor, pose a challenge for most conventional reinforcement learning (RL) algorithms. Algorithms such as Value Iteration and Tempor…
When does Self-Prediction help? Understanding Auxiliary Tasks in Reinforcement Learning
Claas Voelcker, Tyler Kastner, Igor Gilitschenski +1
We investigate the impact of auxiliary learning tasks such as observation reconstruction and latent self-prediction on the representation learning problem in reinforcement learning…