On Oracle-Efficient PAC Reinforcement Learning with Rich Observations

We study the computational tractability of provably sample-efficient (PAC) reinforcement learning in episodic environments with rich observations. We present new 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. In the more general stochastic transition setting, we prove that the only known sample-efficient algorithm, Olive [1], cannot be implemented in our oracle model. We also present several examples that illustrate fundamental challenges of tractable PAC reinforcement learning in such general settings.