6 citations · 21 across the 8 of their papers we have counts for
6 papers · 1 filter
Synthesising Reinforcement Learning Policies through Set-Valued Inductive Rule Learning
Youri Coppens, Denis Steckelmacher, Catholijn M. Jonker +1
Today's advanced Reinforcement Learning algorithms produce black-box policies, that are often difficult to interpret and trust for a person. We introduce a policy distilling algori…
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…
The Actor-Advisor: Policy Gradient With Off-Policy Advice
Hélène Plisnier, Denis Steckelmacher, Diederik M. Roijers +1
Actor-critic algorithms learn an explicit policy (actor), and an accompanying value function (critic). The actor performs actions in the environment, while the critic evaluates the…
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…