- 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 ...
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# 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.
