1.6k citations · 2k across the 10 of their papers we have counts for
13 papers
Recurrent Value Functions
Pierre Thodoroff, Nishanth Anand, Lucas Caccia +2
Despite recent successes in Reinforcement Learning, value-based methods often suffer from high variance hindering performance. In this paper, we illustrate this in a continuous con…
A Deep Reinforcement Learning Chatbot (Short Version)
Iulian V. Serban, Chinnadhurai Sankar, Mathieu Germain +15
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capa…
Ethical Challenges in Data-Driven Dialogue Systems
Peter Henderson, Koustuv Sinha, Nicolas Angelard-Gontier +4
The use of dialogue systems as a medium for human-machine interaction is an increasingly prevalent paradigm. A growing number of dialogue systems use conversation strategies that a…
ACtuAL: Actor-Critic Under Adversarial Learning
Anirudh Goyal, Nan Rosemary Ke, Alex Lamb +4
Generative Adversarial Networks (GANs) are a powerful framework for deep generative modeling. Posed as a two-player minimax problem, GANs are typically trained end-to-end on real-v…
OptionGAN: Learning Joint Reward-Policy Options using Generative Adversarial Inverse Reinforcement Learning
Peter Henderson, Wei-Di Chang, Pierre-Luc Bacon +3
Reinforcement learning has shown promise in learning policies that can solve complex problems. However, manually specifying a good reward function can be difficult, especially for…
A Deep Reinforcement Learning Chatbot
Iulian V. Serban, Chinnadhurai Sankar, Mathieu Germain +15
We present MILABOT: a deep reinforcement learning chatbot developed by the Montreal Institute for Learning Algorithms (MILA) for the Amazon Alexa Prize competition. MILABOT is capa…