
On the Sample Complexity of Reinforcement Learning with Policy Space Generalization
We study the optimal sample complexity in largescale Reinforcement Lear...
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Extracting Latent State Representations with Linear Dynamics from Rich Observations
Recently, many reinforcement learning techniques were shown to have prov...
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Provably Efficient Qlearning with Function Approximation via Distribution Shift Error Checking Oracle
Qlearning with function approximation is one of the most popular method...
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Learning to Guide Random Search
We are interested in derivativefree optimization of highdimensional fu...
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DeepMDP: Learning Continuous Latent Space Models for Representation Learning
Many reinforcement learning (RL) tasks provide the agent with highdimen...
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Demixing Structured Superposition Signals from Periodic and Aperiodic Nonlinear Observations
We consider the demixing problem of two (or more) structured highdimens...
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Beta DVBF: Learning StateSpace Models for Control from High Dimensional Observations
Learning a model of dynamics from highdimensional images can be a core ...
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Learning the Linear Quadratic Regulator from Nonlinear Observations
We introduce a new problem setting for continuous control called the LQR with Rich Observations, or RichLQR. In our setting, the environment is summarized by a lowdimensional continuous latent state with linear dynamics and quadratic costs, but the agent operates on highdimensional, nonlinear observations such as images from a camera. To enable sampleefficient learning, we assume that the learner has access to a class of decoder functions (e.g., neural networks) that is flexible enough to capture the mapping from observations to latent states. We introduce a new algorithm, RichID, which learns a nearoptimal policy for the RichLQR with sample complexity scaling only with the dimension of the latent state space and the capacity of the decoder function class. RichID is oracleefficient and accesses the decoder class only through calls to a leastsquares regression oracle. Our results constitute the first provable sample complexity guarantee for continuous control with an unknown nonlinearity in the system model and general function approximation.
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