Recurrent Neural Networks (RNNs) play a critical role in reinforcement learning (RL) by enabling agents to process sequential data and maintain memory of past states. Unlike feedforward neural networks, which process inputs independently, RNNs use loops to retain information across time steps. This makes them particularly useful in RL scenarios where an agent’s decisions depend on historical context, such as in partially observable environments or tasks requiring long-term planning. For example, in a game where an agent only observes part of the environment at each step, an RNN can track hidden patterns in past observations to infer the full state of the game.
A key application of RNNs in RL is handling partial observability. In many real-world problems, like robotics or navigation, sensors provide incomplete data about the environment. An RNN-based RL agent can process a sequence of observations (e.g., a robot’s sensor readings over time) and build an internal representation of the environment’s state. For instance, a drone navigating through a dynamic environment might use an RNN to remember wind patterns or obstacles encountered earlier, allowing it to adjust its path more effectively. This memory mechanism is often implemented using architectures like Long Short-Term Memory (LSTM) or Gated Recurrent Units (GRUs), which mitigate the vanishing gradient problem and enable stable training over long sequences.
RNNs also help RL agents model temporal dependencies in decision-making. In tasks like dialogue systems or strategy games, actions have delayed consequences that require planning multiple steps ahead. For example, an RL agent trained to play a turn-based game might use an RNN to analyze sequences of moves and predict opponents’ strategies. Algorithms like Deep Recurrent Q-Networks (DRQN) extend traditional Q-learning by replacing feedforward layers with RNNs, allowing the agent to learn policies that account for historical context. Similarly, in policy gradient methods, RNNs enable agents to generate action sequences (e.g., generating text or controlling a character in a game) by conditioning each decision on prior states. By integrating temporal reasoning, RNNs help RL systems tackle complex, real-time problems that demand memory and sequential processing.