In order to overcome overestimation bias, ensemble methods for Q-learning have been investigated to exploit the diversity of multiple Q-functions. Since network initialization has been the predominant approach to promote diversity in Q-functions, heuristically designed diversity injection methods have been studied in the literature. However, previous studies have not attempted to approach guaranteed independence over an ensemble from a theoretical perspective.

The high-capacity generative models trained on large, diverse datasets have demonstrated remarkable success across vision and language tasks.

Contextual batched bandit (CBB) is a setting where a batch of rewards is observed from the environment at the end of each episode, but the rewards of the non-executed actions are unobserved, resulting in partial-information feedback.

We integrate this model in a planning loop, where conditioning and classifier guidance let us softly break the symmetry for specific tasks as needed.

We use induction to prove this statement. The penultimate step follows from the induction hypothesis completing the proof. Then, the fixed point of Eq.(5) is the value function of in f M . We focus on permanent value function in the next two theorems. The permanent value function is updated using Eq.

Deep reinforcement learning (RL) has achieved remarkable successes in complex tasks, e.g Go [

Furthermore, the learned communication protocols exhibit zero-shot generalization capabilities in ad-hoc teamwork scenarios with unseen teammates and novel task states.

This procedure can leverage noisy, asynchronous human feedback to learn policies with no hand-crafted reward design or exploration bonuses.