Transfer / meta / lifelong learning

• RL with policy advice. Azar et al., ECML 2013.

    - Reduction from RL to bandit problem.
  

• Regret bounds: sum of differences between actual policy and optimal policy.

• Regret scales with the number of tasks \sqrt(M), rather than the state and action space.

• Brunskill and Li, UAI 2013. Reduce from RL to (active) classification problem.

• https://cs.stanford.edu/people/ebrun

• Provably speeding multitask RL. Guo and Brunskill, AAAI 2015. K tasks sampled from M tasks. Evaluation goal: provably improve performance. Approach: quickly cluster, then share.

• Killian et al., NIPS 2017. Bayesian NNs for modeling MDP dynamics.

• Smooth latent policy space for crossdomain transfer. Anmar et al., IJCAI 2015. Limited theoretical results (some nice convergence results).

• Model-agnostic meta-learning. Finn et al., ICML 2017.