On Polynomial Time PAC Reinforcement Learning with Rich Observations

By Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, J. Langford, Robert E. Schapire. 2018

9 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Wen Sun
2. Sergey Levine
3. Ruosong Wang
4. Byron Boots
5. Sham M. Kakade
6. Simon Shaolei Du
7. J. Bagnell
8. Alekh Agarwal

Citations in the corpus

• Dual Policy Iteration (2018, background): “, without good initial reset distribution ρ0) for large scale model-free RL is challenging [9] and beyond the scope of this work.”
• Is a Good Representation Sufficient for Sample Efficient Reinforcement Learning? (2019, methodology): “Later work gave computationally efficient algorithms for certain special cases (Dann et al., 2018; Du et al., 2019a; Yang & Wang, 2019b; Jin et al., 2019).”
• Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle (2019, background): “Finally, a recent series of work introduced contextual decision processes (CDPs) [22, 17, 9, 31, 11] and developed algorithms with polynomial sample complexity guarantees.”