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Framing Reinforcement Learning problems efficiently
Understanding the fundamental building blocks of Transformers.
Learn to vectorize an environment and train 30 Q-learning agents in parallel on a CPU, at 1.8 million iterations per second.
Comparing model-free and model-based RL methods on a dynamic grid world