Poster
On Oracle-Efficient PAC RL with Rich Observations
Christoph Dann · Nan Jiang · Akshay Krishnamurthy · Alekh Agarwal · John Langford · Robert Schapire

Wed Dec 5th 10:45 AM -- 12:45 PM @ Room 517 AB #111

We study the computational tractability of PAC reinforcement learning with rich observations. We present new provably sample-efficient algorithms for environments with deterministic hidden state dynamics and stochastic rich observations. These methods operate in an oracle model of computation -- accessing policy and value function classes exclusively through standard optimization primitives -- and therefore represent computationally efficient alternatives to prior algorithms that require enumeration. With stochastic hidden state dynamics, we prove that the only known sample-efficient algorithm, OLIVE, cannot be implemented in the oracle model. We also present several examples that illustrate fundamental challenges of tractable PAC reinforcement learning in such general settings.

Author Information

Christoph Dann (Carnegie Mellon University)
Nan Jiang (University of Illinois at Urbana-Champaign)
Akshay Krishnamurthy (Microsoft)
Alekh Agarwal (Microsoft Research)
John Langford (Microsoft Research New York)
Robert Schapire (MIcrosoft Research)

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