4 papers · 1 filter
Gradient-Based Program Synthesis with Neurally Interpreted Languages
Matthew V. Macfarlane, Clément Bonnet, Herke van Hoof +1
A central challenge in program induction has long been the trade-off between symbolic and neural approaches. Symbolic methods offer compositional generalisation and data efficiency…
Searching Latent Program Spaces
Matthew V Macfarlane, Clement Bonnet
General intelligence requires systems that acquire new skills efficiently and generalize beyond their training distributions. Although program synthesis approaches have strong gene…
Debiasing Meta-Gradient Reinforcement Learning by Learning the Outer Value Function
Clément Bonnet, Laurence Midgley, Alexandre Laterre
Meta-gradient Reinforcement Learning (RL) allows agents to self-tune their hyper-parameters in an online fashion during training. In this paper, we identify a bias in the meta-grad…
One Step at a Time: Pros and Cons of Multi-Step Meta-Gradient Reinforcement Learning
Clément Bonnet, Paul Caron, Thomas Barrett +2
Self-tuning algorithms that adapt the learning process online encourage more effective and robust learning. Among all the methods available, meta-gradients have emerged as a promis…