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LL-VQ-VAE: Learnable Lattice Vector-Quantization For Efficient Representations
In this paper we introduce learnable lattice vector quantization and demonstrate its effectiveness for learning discrete representations.
Q-learning Decision Transformer: Leveraging Dynamic Programming for Conditional Sequence Modelling in Offline RL
The Decision Transformer (DT) combines the conditional policy approach and a transformer architecture, showing competitive performance against several benchmarks.
RLOps: Development Life-cycle of Reinforcement Learning Aided Open RAN
We provide a taxonomy for the challenges faced by ML/RL models throughout the development life-cycle: from the system specification to production deployment (data acquisition, model design, testing and management, etc.).
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