Reinforcement learning by backpropagation through an LSTM model/critic

This paper describes backpropagation through an LSTM recurrent neural network model/critic, for reinforcement learning tasks in partially observable domains. This combines the advantage of LSTM's strength at learning long-term temporal dependencies to infer states in partially observable tasks, with the advantage of being able to learn high-dimensional and/or continuous actions with backpropagation's focused credit assignment mechanism