arXiv:2605.05481v2 Announce Type: replace Abstract: We revisit a classic "chicken-and-egg" problem in reinforcement learning: to safely improve a policy, the value function must be accurate on the state-visitation distribution of the updated policy. That distribution over states is unknown and cannot be sampled for the purposes of training the value function. Conservative updates solve this problem, but at the cost of shrinking the policy update. This paper explores an alternative solution, Approximate Next Policy Sampling (ANPS), which addresses the problem by modifying the training distribut
The paper addresses a fundamental challenge in deep reinforcement learning, a field undergoing rapid theoretical and practical advancements.
Improving policy updates in deep RL accelerates the development of more capable and efficient AI systems, impacting various applications.
The proposed 'Approximate Next Policy Sampling' method offers an alternative to conservative updates, potentially leading to faster and safer policy improvement in RL.
- · AI researchers
- · Deep RL application developers
- · Robotics and autonomous systems
- · Inefficient RL algorithms
- · Conservative policy update methods
More robust and efficient training of AI agents in complex environments.
Accelerated development of AI systems capable of learning and adapting with fewer safety constraints.
Potentially enables more sophisticated AI agents in critical applications like infrastructure management or defense.
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