Scalability and Efficiency in RL
An analysis of IMPALA and HER to make RL faster and more efficient at scale..
Chapter 51: Distributed RL Problem Definition and Research Motivation Research Directions: Parallelization, scalability Systems: Distributed frameworks, cloud-based RL Algorithms: Ape-X, IMPALA, R2D2 Future Study: Decentralized systems, resource efficiency References Chapter 52: Sample Efficiency in RL (Hindsight experience replay, data augmentation, off-policy learning) Chapter 53: Scalable Policy Optimization (SAC, TD3, D4PG, PPO variants, constrained optimization) Chapter 54: Hardware Acceleration for RL (GPUs, TPUs, custom RL accelerators, simulation optimization)