# DOP: Deep Optimistic Planning with Approximate Value Function Evaluation

## TL;DR

DOP is a deep model-based reinforcement learning algorithm that uses learned Q-functions to guide exploration and planning, effectively handling high-dimensional, uncertain scenarios in robotics.

## Contribution

This paper introduces DOP, a novel deep RL method that leverages approximate Q-functions for efficient exploration and planning in complex robotic tasks.

## Key findings

- DOP reduces computational demand in planning.
- DOP achieves effective performance in various robotic scenarios.
- DOP successfully applied in simulation and real-world tasks.

## Abstract

Research on reinforcement learning has demonstrated promising results in manifold applications and domains. Still, efficiently learning effective robot behaviors is very difficult, due to unstructured scenarios, high uncertainties, and large state dimensionality (e.g. multi-agent systems or hyper-redundant robots). To alleviate this problem, we present DOP, a deep model-based reinforcement learning algorithm, which exploits action values to both (1) guide the exploration of the state space and (2) plan effective policies. Specifically, we exploit deep neural networks to learn Q-functions that are used to attack the curse of dimensionality during a Monte-Carlo tree search. Our algorithm, in fact, constructs upper confidence bounds on the learned value function to select actions optimistically. We implement and evaluate DOP on different scenarios: (1) a cooperative navigation problem, (2) a fetching task for a 7-DOF KUKA robot, and (3) a human-robot handover with a humanoid robot (both in simulation and real). The obtained results show the effectiveness of DOP in the chosen applications, where action values drive the exploration and reduce the computational demand of the planning process while achieving good performance.