1k citations · 1.1k across the 8 of their papers we have counts for
4 papers · 1 filter
Learnings Options End-to-End for Continuous Action Tasks
Martin Klissarov, Pierre-Luc Bacon, Jean Harb +1
We present new results on learning temporally extended actions for continuoustasks, using the options framework (Suttonet al.[1999b], Precup [2000]). In orderto achieve this goal w…
When Waiting is not an Option : Learning Options with a Deliberation Cost
Jean Harb, Pierre-Luc Bacon, Martin Klissarov +1
Recent work has shown that temporally extended actions (options) can be learned fully end-to-end as opposed to being specified in advance. While the problem of "how" to learn optio…
Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments
Ryan Lowe, Yi Wu, Aviv Tamar +3
We explore deep reinforcement learning methods for multi-agent domains. We begin by analyzing the difficulty of traditional algorithms in the multi-agent case: Q-learning is challe…
Investigating Recurrence and Eligibility Traces in Deep Q-Networks
Jean Harb, Doina Precup
Eligibility traces in reinforcement learning are used as a bias-variance trade-off and can often speed up training time by propagating knowledge back over time-steps in a single up…