rewardsparse

Rewardsparse refers to a setting in reinforcement learning where the reward signal is infrequent or only provided after long sequences of actions. In such environments, the agent receives little feedback during most steps, making it challenging to learn which actions lead to distant rewards. This sparse feedback creates a difficult credit assignment problem and often requires many interactions to achieve satisfactory performance. The term commonly appears in discussions of sparse rewards or sparse-reward environments, and may be used in code or literature to describe this characteristic of a task.

Typical features of rewardsparse environments include delayed rewards, minimal intermediate signals, and a high reliance on exploration to discover rewarding states or goals. Tasks in robotics, goal-conditioned manipulation, navigation, and certain game settings frequently exhibit sparse rewards. Because rewards are hard to obtain, naive learning methods may struggle to make progress without extensive experience.

Techniques to mitigate rewardsparse challenges include reward shaping, which adds intermediate, carefully designed feedback signals that guide learning; intrinsic motivation and curiosity to encourage exploration; and goal-relabelling approaches like hindsight experience replay (HER) that make sparse rewards more learnable by reframing past experiences. Curriculum learning, dense auxiliary tasks, and model-based planning can also improve sample efficiency. It is important to balance shaping with preserving the optimal policy, as poorly designed rewards can bias results.

Terminology and scope vary, but rewardsparse is widely understood as describing environments where reward density is low. Related concepts include sparse rewards, reward shaping, intrinsic motivation, and hindsight experience replay.