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Found 7 papers, 2 papers with code
ETHER: Aligning Emergent Communication for Hindsight Experience Replay
We show that the referential game's agents make an artificial language emerge that is aligned with the natural-like language used to describe goals in the BabyAI benchmark and that it is expressive enough so as to also describe unsuccessful RL trajectories and thus provide feedback to the RL agent to leverage the linguistic, structured information contained in all trajectories.
Composing Efficient, Robust Tests for Policy Selection
Empirical results demonstrate that RPOSST finds a small set of test cases that identify high quality policies in a toy one-shot game, poker datasets, and a high-fidelity racing simulator.
BRExIt: On Opponent Modelling in Expert Iteration
Finding a best response policy is a central objective in game theory and multi-agent learning, with modern population-based training approaches employing reinforcement learning algorithms as best-response oracles to improve play against candidate opponents (typically previously learnt policies).
A Comparison of Self-Play Algorithms Under a Generalized Framework
They allow to verify and replicate existing findings, and to link is connected results.
Metagame Autobalancing for Competitive Multiplayer Games
Automated game balancing has often focused on single-agent scenarios.
Nonlinear Evolution via Spatially-Dependent Linear Dynamics for Electrophysiology and Calcium Data
We present Variational Inference for Nonlinear Dynamics (VIND), a variational inference framework that is able to uncover nonlinear, smooth latent dynamics from sequential data.
Learning Socially Appropriate Robot Approaching Behavior Toward Groups using Deep Reinforcement Learning
Deep reinforcement learning has recently been widely applied in robotics to study tasks such as locomotion and grasping, but its application to social human-robot interaction (HRI) remains a challenge.
