5 papers · 1 filter
Highway Value Iteration Networks
Yuhui Wang, Weida Li, Francesco Faccio +2
Value iteration networks (VINs) enable end-to-end learning for planning tasks by employing a differentiable "planning module" that approximates the value iteration algorithm. Howev…
Scaling Value Iteration Networks to 5000 Layers for Extreme Long-Term Planning
Yuhui Wang, Qingyuan Wu, Dylan R. Ashley +4
The Value Iteration Network (VIN) is an end-to-end differentiable neural network architecture for planning. It exhibits strong generalization to unseen domains by incorporating a d…
Highway Reinforcement Learning
Yuhui Wang, Miroslav Strupl, Francesco Faccio +5
Learning from multi-step off-policy data collected by a set of policies is a core problem of reinforcement learning (RL). Approaches based on importance sampling (IS) often suffer…
Variational Delayed Policy Optimization
Qingyuan Wu, Simon Sinong Zhan, Yixuan Wang +5
In environments with delayed observation, state augmentation by including actions within the delay window is adopted to retrieve Markovian property to enable reinforcement learning…
Boosting Reinforcement Learning with Strongly Delayed Feedback Through Auxiliary Short Delays
Qingyuan Wu, Simon Sinong Zhan, Yixuan Wang +6
Reinforcement learning (RL) is challenging in the common case of delays between events and their sensory perceptions. State-of-the-art (SOTA) state augmentation techniques either s…