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Found 4 papers, 1 papers with code
A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games
This paper proposes new, end-to-end deep reinforcement learning algorithms for learning two-player zero-sum Markov games.
Narrowing the Coordinate-frame Gap in Behavior Prediction Models: Distillation for Efficient and Accurate Scene-centric Motion Forecasting
These models are intrinsically invariant to translation and rotation between scene elements, are best-performing on public leaderboards, but scale quadratically with the number of agents and scene elements.
MURO: Deployment Constrained Reinforcement Learning with Model-based Uncertainty Regularized Batch Optimization
In the high support region (low uncertainty), we encourage our policy by taking an aggressive update.
MUSBO: Model-based Uncertainty Regularized and Sample Efficient Batch Optimization for Deployment Constrained Reinforcement Learning
We consider a setting that lies between pure offline reinforcement learning (RL) and pure online RL called deployment constrained RL in which the number of policy deployments for data sampling is limited.
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