rnn

RL² proposes encoding a fast reinforcement learning algorithm as the weights of a recurrent neural network, learned through slow general-purpose RL, enabling agents to adapt to new tasks with few trials similar to biological learning. The method demonstrates strong performance on both small-scale bandit problems and large-scale vision-based navigation tasks.