Deep Recurrent Reinforcement Learning makes use of a Recurrent Neural Network (RNN), such as Long Short-Term Memory (LSTM) or Gated Recurrent Unit (GRU) based networks, for learning a value function that maps environment states to action values. Recurrent Neural Networks are useful for modeling time-series data since the network maintains a memory, learning to retain useful information from inputs of prior model inferences. Every time the model is called, the memory is updated in correspondence with the current inputs.

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Deep Reinforcement Learning involves using a neural network as a universal function approximator to learn a value function that maps state-action pairs to their expected future reward given a particular reward function. This can be done many different ways. For example, a Monte Carlo based algorithm will observe total rewards following state-action pairs from a complete episode to make build training data for the neural network. Alternatively, a Temporal Difference approach would use incremental rewards from single time-steps and bootstrap off of predicted future rewards from the latest version of the value function model. However, no matter what approach is taken, it is important that the neural network is being efficiently fitted to the data in order to optimize the learning algorithm. There are many factors that determine a neural networks ability to fit to training data. In this post we will examine how scaling our outputs can affect our rate of convergence.

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Deep Recurrent Neural Networks (RNN) are a type of Artificial Neural Network that takes the networks previous hidden state as part of its input, effectively allowing the network to have a memory. This makes RNNs useful for modeling sequential or time-series data such as stock prices. However, training RNNs on sequences greater than a few hundred time steps can be difficult. In this post, we will explore three tools that can allow for more efficient training of RNN models with long sequences: Optimizers, Gradient Clipping, and Batch Sequence Length.

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