Approximate Next Policy Sampling: Replacing Conservative Target Policy Updates in Deep RL
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 distribution rather than constraining the policy update. ANPS is satisfied if the distribution of the training data approximates that of the next policy. To d...
| Authors | Dillon Sandhu, Ronald Parr |
|---|---|
| Primary category | cs.LG |
| Published | 6 May 2026 |
| Last updated at source | 17 June 2026 |
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