Sparse Reward
A reward setup in reinforcement learning in which a success or failure signal is given only at the end of the task, with no feedback in intermediate steps. Its advantage is low authoring cost; its disadvantages are difficulty in credit assignment and scarce learning signal.
Core Facts
Timeline (last 90 days)
The advantages of sparse rewards are low authoring cost and resistance to contamination by subjective bias; the disadvantage is that credit assignment is difficult, making it hard to determine which step in a long action sequence caused the failure.
Sparse rewards only provide signals when a task succeeds or fails, which leads to extremely low exploration efficiency in long-horizon, multi-stage tasks.
In sparse-reward scenarios, agents often need techniques such as curriculum learning, reward shaping, or intrinsic motivation-based exploration to converge effectively.
Long-horizon planning and sparse rewards are among the most difficult pain points for current reinforcement learning algorithms to overcome
All Facts (4)
In sparse-reward scenarios, agents often need techniques such as curriculum learning, reward shaping, or intrinsic motivation-based exploration to converge effectively.
75%UnverifiedThe advantages of sparse rewards are low authoring cost and resistance to contamination by subjective bias; the disadvantage is that credit assignment is difficult, making it hard to determine which step in a long action sequence caused the failure.
50%UnverifiedSparse rewards only provide signals when a task succeeds or fails, which leads to extremely low exploration efficiency in long-horizon, multi-stage tasks.
50%UnverifiedLong-horizon planning and sparse rewards are among the most difficult pain points for current reinforcement learning algorithms to overcome
50%