Recurrent neural networks (RNNs) play an important role in reinforcement learning when the agent needs to maintain a memory of past states or actions to make decisions. Unlike traditional feedforward neural networks, RNNs have internal loops that allow them to retain information about previous time steps. This makes RNNs suitable for environments where the current decision depends not only on the current state but also on the sequence of past states or actions (e.g., partially observable environments).
In reinforcement learning, RNNs can be used to approximate the policy or value function in environments where the state is partially observable (e.g., in a game with hidden information or in real-world robotics with noisy sensors). For example, in partially observable Markov decision processes (POMDPs), RNNs can help the agent remember key information from earlier in the episode.
RNNs, particularly Long Short-Term Memory (LSTM) networks, are commonly used in situations where temporal dependencies are crucial for making accurate predictions about future states or actions.