Decentralized MARL approach using distribution matching for cooperative multi-agent coordination

Estimating and maximizing the Wasserstein distance from a start state to learn skills in a controlled Markov process

Estimating and minimizing the Wasserstein distance to an idealized target distribution to learn a goal-conditioned policy

Using Imitation from Observation techniques to speed up transfer of policies between environments with dynamics mismatch

A mixing scheme that balances individual agent preferences with shared objectives and studies the subsequent learning behavior

Reducing the error due to sampling in batch TD learning

Using policy gradient to learn navigation in knowledge bases

Constraining TD updates to improve stability

Improving GAN training stability and quality with multiple discriminators

Predicting nonstationarity in changing real-world scenarios for off-policy evaluation

Mapping VAE decoder samples back to latent space for improved generative accuracy

Extending DQN with temporally extended macro-actions for improved exploration and learning

Swarm-inspired technique for convex optimization using cohort-based self-supervised learning