Elementary decision theory
Using expectation-maximization for reinforcement learning
Neural Computation
Learning predictive state representations using non-blind policies
ICML '06 Proceedings of the 23rd international conference on Machine learning
Neural Computation
Reinforcement learning by reward-weighted regression for operational space control
Proceedings of the 24th international conference on Machine learning
Infinite-horizon policy-gradient estimation
Journal of Artificial Intelligence Research
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Solving deep memory POMDPs with recurrent policy gradients
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
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It has been a long-standing goal in the adaptive control community to reduce the generically difficult, general reinforcement learning (RL) problem to simpler problems solvable by supervised learning. While this approach is today's standard for value function-based methods, fewer approaches are known that apply similar reductions to policy search methods. Recently, it has been shown that immediate RL problems can be solved by reward-weighted regression, and that the resulting algorithm is an expectation maximization (EM) algorithm with strong guarantees. In this paper, we extend this algorithm to the episodic case and show that it can be used in the context of LSTM recurrent neural networks (RNNs). The resulting RNN training algorithm is equivalent to a weighted self-modeling supervised learning technique. We focus on partially observable Markov decision problems (POMDPs) where it is essential that the policy is nonstationary in order to be optimal. We show that this new reward-weighted logistic regression used in conjunction with an RNN architecture can solve standard benchmark POMDPs with ease.