11 citations · 15 across the 3 of their papers we have counts for
5 papers
Learning to Play Imperfect-Information Games by Imitating an Oracle Planner
Rinu Boney, Alexander Ilin, Juho Kannala +1
We consider learning to play multiplayer imperfect-information games with simultaneous moves and large state-action spaces. Previous attempts to tackle such challenging games have…
Learning to Drive (L2D) as a Low-Cost Benchmark for Real-World Reinforcement Learning
Ari Viitala, Rinu Boney, Yi Zhao +2
We present Learning to Drive (L2D), a low-cost benchmark for real-world reinforcement learning (RL). L2D involves a simple and reproducible experimental setup where an RL agent has…
Regularizing Model-Based Planning with Energy-Based Models
Rinu Boney, Juho Kannala, Alexander Ilin
Model-based reinforcement learning could enable sample-efficient learning by quickly acquiring rich knowledge about the world and using it to improve behaviour without additional d…
Regularizing Trajectory Optimization with Denoising Autoencoders
Rinu Boney, Norman Di Palo, Mathias Berglund +4
Trajectory optimization using a learned model of the environment is one of the core elements of model-based reinforcement learning. This procedure often suffers from exploiting ina…
Recurrent Ladder Networks
Isabeau Prémont-Schwarz, Alexander Ilin, Tele Hotloo Hao +3
We propose a recurrent extension of the Ladder networks whose structure is motivated by the inference required in hierarchical latent variable models. We demonstrate that the recur…