6 citations · 11 across the 4 of their papers we have counts for
5 papers
Transfer Learning Across Simulated Robots With Different Sensors
Hélène Plisnier, Denis Steckelmacher, Diederik Roijers +1
For a robot to learn a good policy, it often requires expensive equipment (such as sophisticated sensors) and a prepared training environment conducive to learning. However, it is…
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…
Analysing Congestion Problems in Multi-agent Reinforcement Learning
Roxana Rădulescu, Peter Vrancx, Ann Nowé
Congestion problems are omnipresent in today's complex networks and represent a challenge in many research domains. In the context of Multi-agent Reinforcement Learning (MARL), app…
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…