arXiv:2605.20878v1 Announce Type: new Abstract: Intrinsic rewards for exploration in reinforcement learning condition on different contexts: lifelong rewards score each transition against accumulated experience but ignore within-rollout redundancy; episodic rewards penalize intra-trajectory repetition but discard lifetime progress. Hybrid methods combine both signals through heuristic weights or require Gaussian-process dynamics that do not scale beyond low-dimensional state spaces. Trajectory-level information gain decomposes into per-step terms that condition on the replay buffer and rollout
The paper addresses a core challenge in reinforcement learning (RL) exploration, a field experiencing rapid advancements crucial for scaling AI capabilities.
Improved exploration methods in RL lead to more efficient and robust learning, directly impacting the development of advanced AI systems, including autonomous agents.
This research proposes a new method (CIG) for intrinsic rewards in RL, potentially overcoming limitations of existing methods and offering more scalable solutions for complex tasks.
- · AI agents developers
- · Reinforcement learning researchers
- · Robotics companies
- · Autonomous systems developers
- · Companies relying on less efficient RL exploration techniques
- · Developers stuck with computationally intensive methods
More efficient and generalizable reinforcement learning models are developed.
This leads to faster progress in training complex AI agents and autonomous systems.
Advanced AI agents begin to automate more sophisticated tasks previously requiring human intervention, accelerating workflow collapses.
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Read at arXiv cs.LG