arXiv:2606.27180v1 Announce Type: new Abstract: Sparse rewards are inherently challenging for reinforcement learning agents as they lack intermediate feedback to guide exploration and to correctly attribute the sparse success rewards to relevant parts of the trajectory. Naive reward shaping can induce reward hacking, yielding policies that exploit auxiliary signals instead of solving the intended task. Potential-based reward shaping (PBRS) guarantees preservation of the optimal policy set, but requires the definition of a heuristic potential function over the state space. In this work, we intr
The increasing complexity and practical deployment challenges of reinforcement learning systems, particularly concerning reward design, necessitate robust automated solutions like Vision Language Models (VLMs) to accelerate progress.
Automating reward shaping for reinforcement learning agents mitigates a significant obstacle to developing more capable and generalizable AI, potentially expanding their applicability across various domains.
The reliance on manual, expert-driven reward function design in reinforcement learning could decrease, leading to faster development cycles and more robust, less exploitable AI agents.
- · AI developers
- · Robotics industry
- · Companies using RL for complex task automation
- · Manual RL reward engineers
More efficient and generalizable reinforcement learning agents will be developed.
AI agents could solve complex, real-world tasks with less human intervention and fewer design flaws.
This could accelerate the deployment of autonomous systems into new and safety-critical environments.
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