223 citations · 267 across the 16 of their papers we have counts for
6 papers · 1 filter
KernelArc: A Multi-Agent Framework for GPU Kernel Optimization
Joyjit Kundu, Ben Stoffelen, Kaili Wang +2
We present KernelArc, a multi-agent framework for autonomous GPU kernel optimization across heterogeneous workloads. Strategy-specialized agents run in parallel and coordinate thro…
Learning High-level Representations from Demonstrations
Garrett Andersen, Peter Vrancx, Haitham Bou-Ammar
Hierarchical learning (HL) is key to solving complex sequential decision problems with long horizons and sparse rewards. It allows learning agents to break-up large problems into s…
Learning with Options that Terminate Off-Policy
Anna Harutyunyan, Peter Vrancx, Pierre-Luc Bacon +2
A temporally abstract action, or an option, is specified by a policy and a termination condition: the policy guides option behavior, and the termination condition roughly determine…
Reinforcement Learning in POMDPs with Memoryless Options and Option-Observation Initiation Sets
Denis Steckelmacher, Diederik M. Roijers, Anna Harutyunyan +3
Many real-world reinforcement learning problems have a hierarchical nature, and often exhibit some degree of partial observability. While hierarchy and partial observability are us…
Off-Policy Reward Shaping with Ensembles
Anna Harutyunyan, Tim Brys, Peter Vrancx +1
Potential-based reward shaping (PBRS) is an effective and popular technique to speed up reinforcement learning by leveraging domain knowledge. While PBRS is proven to always preser…
Off-Policy Shaping Ensembles in Reinforcement Learning
Anna Harutyunyan, Tim Brys, Peter Vrancx +1
Recent advances of gradient temporal-difference methods allow to learn off-policy multiple value functions in parallel with- out sacrificing convergence guarantees or computational…