# Predictive State Temporal Difference Learning

@inproceedings{Boots2010PredictiveST, title={Predictive State Temporal Difference Learning}, author={Byron Boots and Geoffrey J. Gordon}, booktitle={NIPS}, year={2010} }

We propose a new approach to value function approximation which combines linear temporal difference reinforcement learning with subspace identification. In practical applications, reinforcement learning (RL) is complicated by the fact that state is either high-dimensional or partially observable. Therefore, RL methods are designed to work with features of state rather than state itself, and the success or failure of learning is often determined by the suitability of the selected features. By…

## 48 Citations

### Spectral Approaches to Learning Predictive Representations

- Computer Science
- 2011

A central problem in artificial intelligence is to choose actions to maximize reward in a partially observable, uncertain environment. To do so, we must obtain an accurate environment model, and then…

### Incremental Basis Construction from Temporal Difference Error

- Computer ScienceICML
- 2011

This result suggests a novel method for improving value-function estimation: a primary reinforcement learner estimates its value function using its present basis functions; it then sends its TD error to a secondary learner, which interprets that error as a reward function and estimates the corresponding value function.

### Practical Learning of Predictive State Representations

- Computer ScienceArXiv
- 2017

Inference Gradients, a simple, fast, and robust method for practical learning of PSRs, which combines spectral algorithms for PSRs as a consistent and efficient initialization with PSIM-style updates to refine the resulting model parameters.

### An Online Spectral Learning Algorithm for Partially Observable Nonlinear Dynamical Systems

- Computer ScienceAAAI
- 2011

A new online spectral algorithm is proposed, which uses tricks such as incremental Singular Value Decomposition (SVD) and random projections to scale to much larger data sets and more complex systems than previous methods.

### Efficient learning and planning with compressed predictive states

- Computer ScienceJ. Mach. Learn. Res.
- 2014

The notion of compressed PSRs (CPSRs) is introduced, and it is shown how this approach provides a principled avenue for learning accurate approximations of PSRs, drastically reducing the computational costs associated with learning while also providing effective regularization.

### Efficient Methods for Prediction and Control in Partially Observable Environments

- Computer Science
- 2018

The proposed framework for constructing state estimators enjoys a number of theoretical and practical advantages over existing methods, and it is demonstrated its efficacy in a prediction setting, where the task is to predict future observations, as well as a control setting, which is to optimize a control policy via reinforcement learning.

### Thesis Proposal: Efficient and Tractable Methods for System Identification through Supervised Learning

- Computer Science
- 2017

This work develops a class of dynamical systems and an associated learning meta-algorithm resulting in a framework for system identification that enjoys several theoretical and practical advantages and results in an efficient and local minima-free method for learning non-linear partially observable continuous systems.

### Learning Dynamic Policies from Demonstration

- Computer Science
- 2013

It is shown that system identification algorithms with desirable properties like the ability to model long-range dependancies, statistical consistency, and efficient off-the-shelf implementations can be carried over to the learning from demonstration domain.

### On the generation of representations for reinforcement learning

- Computer Science
- 2012

It is proved that under certain technical conditions, the size of the dictionary will always grow sub-linearly in the number of data points, and, as a consequence, the kernel linear regressor or value function estimator constructed from the resulting dictionary is consistent.

### Learning to Filter with Predictive State Inference Machines

- Computer ScienceICML
- 2016

This work presents the PREDICTIVE STATE INFERENCE MACHINE (PSIM), a data-driven method that considers the inference procedure on a dynamical system as a composition of predictors and directly learns predictors for inference in predictive state space.

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This work introduces an interesting construct, the system-dynamics matrix, and shows how PSRs can be derived simply from it, and uses this construct to show formally that PSRs are more general than both nth-order Markov models and HMMs/POMDPs.