Handling sparse rewards in reinforcement learning (RL) is challenging because the agent receives little or no feedback on most actions, making it difficult to learn effective policies. Sparse rewards occur in environments where the agent only gets a reward upon achieving a rare, high-level goal (e.g., winning a game or solving a puzzle). Without intermediate feedback, the agent may struggle to explore effectively or discover meaningful behaviors. To address this, developers use techniques like reward shaping, intrinsic motivation, and curriculum learning.
Reward shaping modifies the environment’s reward structure to provide intermediate guidance. For example, in a maze-solving task, instead of only rewarding the agent for reaching the exit, you might add small rewards for moving closer to the goal. This helps the agent learn incremental progress. However, designing these rewards requires domain knowledge and risks unintended behavior if the shaped rewards misalign with the true objective. Tools like potential-based reward shaping can mitigate this by ensuring the added rewards don’t create local optima that distract from the main goal. For instance, in robotics, a robot learning to grasp an object might receive rewards for reducing distance to the target, even if the final grasp is rare.
Intrinsic motivation encourages exploration by rewarding the agent for discovering novel states or reducing uncertainty. Methods like Random Network Distillation (RND) or curiosity-driven exploration use a separate neural network to predict the outcome of the agent’s actions, rewarding states where predictions are inaccurate (i.e., the agent is in unfamiliar territory). For example, in a game like Montezuma’s Revenge, where rewards are sparse, curiosity-driven agents explore more efficiently by seeking rooms or interactions they haven’t seen before. Another approach is count-based exploration, which tracks how often states are visited and rewards the agent for entering less frequented states. These methods help the agent explore even when external rewards are absent.
Curriculum learning and hierarchical RL break the problem into manageable steps. A curriculum starts with simpler tasks (e.g., moving toward a nearby object) and gradually increases difficulty (e.g., navigating complex terrain). Hierarchical RL divides the task into sub-goals, where each sub-goal (e.g., opening a door before searching for a key) provides intermediate rewards. For instance, a delivery robot might first learn to navigate to a room, then locate a specific shelf. Frameworks like Hindsight Experience Replay (HER) also help by allowing the agent to learn from failed attempts. In HER, when a goal isn’t achieved, the agent treats the achieved state as a new goal, enabling it to learn from partial progress. These approaches reduce reliance on sparse rewards by creating structured learning pathways.