Self-Balancing Shaped Rewards for Sparse Task Training
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Solution Overview
Problem
Existing methods for training deep learning systems on sparse reward tasks often require problem-specific reward functions, leading to local optima issues and poor generalization, especially in tasks like maze navigation and 3D construction, where naive distance-to-goal reward shaping fails.
Innovation Solution
A model-free approach using self-balancing shaped rewards based on sibling trajectories to encourage diverse exploration and avoid local optima, without requiring domain-specific knowledge, by relabeling rewards and updating gradients iteratively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If naive distance-to-goal reward shaping is used, then the training process provides continuous feedback, but the system converges to local optima instead of global optima
Solution Approach 1:
The patent inverts the traditional reward shaping approach by using achieved goals (terminal states) as new target goals for reward calculation. Instead of rewarding distance to the original goal, the system rewards distance to the achieved state, which is often a local optimum. This inversion prevents convergence to local optima by making them unrewarding targets while still providing continuous feedback for learning.
Solution Approach 2:
The system implements a feedback mechanism where the achieved terminal state of each trajectory becomes the reference for calculating shaped rewards in subsequent training iterations. This dynamic feedback loop allows the agent to learn from its own performance history, continuously adjusting the reward landscape to encourage exploration beyond local optima while maintaining reliable task completion.
2Reliability
If problem-specific reward functions are engineered, then local optima issues are addressed for specific tasks, but the solution does not generalize to other tasks
Solution Approach 1:
The patent applies a universal reward shaping method that works across multiple task domains (maze navigation, 3D construction, etc.) without requiring task-specific customization. The core mechanism of using achieved goals as dynamic targets is domain-agnostic and can be applied to any goal-reaching task, eliminating the need for complex problem-specific reward engineering while maintaining high performance.
Solution Approach 2:
The system is self-adaptive and does not require external domain expertise to design task-specific rewards. The achieved terminal states automatically serve as the basis for reward calculation, allowing the system to self-adjust to different task environments without human intervention or specialized knowledge of each task domain.
3Ease of manufacture
If sparse reward functions are used, then the task definition remains simple, but the training process requires excessive exploration time
Solution Approach 1:
The system performs preliminary action by using the achieved terminal state as a pre-computed reference point for reward calculation. This preliminary determination of target states allows the system to provide immediate shaped feedback during training, eliminating the need for extensive random exploration that would otherwise be required with sparse rewards, while keeping the task definition simple.
Data Source
AI summary
Approaches for using self-balancing shaped rewards include randomly selecting a start and goal state, traversing first and second trajectories for moving from the start state toward the goal state where a first terminal state of the first trajectory is closer to the goal state than a second terminal state of the second trajectory, updating rewards for the first and trajectories using a self-balancing reward function based the terminal states of the other trajectory, determining a gradient for the goal-oriented task module, and updating one or more parameters of the goal-oriented task module based on the gradient. The second trajectory contributes to the determination of the gradient and the first trajectory contributes to the determination of the gradient when the first terminal state is within a first threshold distance of the second terminal state or the first terminal state is within a second threshold distance of the goal state.