Few-shot learning in reinforcement learning (RL) enables agents to adapt quickly to new tasks with minimal experience, often by leveraging prior knowledge from related tasks. Traditional RL requires extensive interaction with an environment to learn effective policies, which can be impractical in scenarios where data is scarce or costly to collect. Few-shot learning addresses this by training agents to generalize from a small number of examples or trials. This is achieved by designing algorithms that either pre-train on a diverse set of tasks or use meta-learning techniques to extract reusable knowledge, allowing the agent to adjust rapidly to novel situations with limited additional data.
A common approach is meta-reinforcement learning (meta-RL), where an agent is trained across multiple tasks to learn a policy or adaptation strategy that can be fine-tuned quickly. For example, an agent might learn to navigate various mazes during meta-training, then use just a few episodes in a new maze to adapt its strategy. Algorithms like Model-Agnostic Meta-Learning (MAML) are adapted for RL by optimizing initial policy parameters that can be fine-tuned efficiently via a few gradient steps on new tasks. Another method involves hierarchical policies, where a high-level controller learns to compose low-level skills (e.g., “move forward” or “turn left”) in new combinations for unseen tasks. In robotics, this could enable a robot arm to learn to manipulate new objects with only a handful of demonstrations.
The benefits of few-shot RL include reduced training time and better sample efficiency, but challenges remain. For instance, meta-RL requires a diverse set of training tasks to ensure generalization, which may not always be available. Additionally, balancing exploration (trying new actions) and exploitation (using known strategies) becomes harder with limited trials. Practical applications include game AI adapting to new levels, drones learning navigation in unfamiliar environments, or industrial robots handling varying assembly tasks. To implement this, developers often use frameworks like RLlib or OpenAI Gym, combined with meta-learning libraries. Key considerations include designing task distributions that reflect real-world variability and ensuring the agent’s architecture (e.g., recurrent networks for memory) supports rapid adaptation.