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Found 7 papers, 3 papers with code
K-level Reasoning for Zero-Shot Coordination in Hanabi
The standard problem setting in cooperative multi-agent settings is self-play (SP), where the goal is to train a team of agents that works well together.
Self-Explaining Deviations for Coordination
Fully cooperative, partially observable multi-agent problems are ubiquitous in the real world.
CompilerGym: Robust, Performant Compiler Optimization Environments for AI Research
What is needed is an easy, reusable experimental infrastructure for real world compiler optimization tasks that can serve as a common benchmark for comparing techniques, and as a platform to accelerate progress in the field.
Learning Space Partitions for Path Planning
Path planning, the problem of efficiently discovering high-reward trajectories, often requires optimizing a high-dimensional and multimodal reward function.
Off-Belief Learning
Policies learned through self-play may adopt arbitrary conventions and implicitly rely on multi-step reasoning based on fragile assumptions about other agents' actions and thus fail when paired with humans or independently trained agents at test time.
Control-Aware Representations for Model-based Reinforcement Learning
We first formulate a LCE model to learn representations that are suitable to be used by a policy iteration style algorithm in the latent space.
Model-based Reinforcement Learning
reinforcement-learning
+2
Variational Model-based Policy Optimization
Model-based reinforcement learning (RL) algorithms allow us to combine model-generated data with those collected from interaction with the real system in order to alleviate the data efficiency problem in RL.
