Neighborhood curiosity-based exploration in multi-agent reinforcement learning
S Yang, Z He, J Li, H Shi, Q Ji… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Efficient exploration in cooperative multi-agent reinforcement learning is still tricky in
complex tasks. In this paper, we propose a novel multi-agent collaborative exploration …
complex tasks. In this paper, we propose a novel multi-agent collaborative exploration …
Denoised Predictive Imagination: An Information-theoretic approach for learning World Models
V Dave, E Rueckert - Seventeenth European Workshop on Reinforcement … - openreview.net
Humans excel at isolating relevant information from noisy data to predict the behavior of
dynamic systems, effectively disregarding non-informative, temporally-correlated noise. In …
dynamic systems, effectively disregarding non-informative, temporally-correlated noise. In …
Information-Theoretic World Model learning for Denoised Predictions
V Dave, E Rueckert - openreview.net
Humans excel at isolating relevant information from noisy data to predict the behavior of
dynamic systems, effectively disregarding non-informative, temporally-correlated noise. In …
dynamic systems, effectively disregarding non-informative, temporally-correlated noise. In …