Titlebook: Deep Reinforcement Learning; Fundamentals, Resear Hao Dong,Zihan Ding,Shanghang Zhang Book 2020 Springer Nature Singapore Pte Ltd. 2020 Dee

Agility

Multi-Agent Reinforcement Learningeasing the number of agents brings in the challenges on managing the interactions among them. In this chapter, according to the optimization problem for each agent, equilibrium concepts are put forward to regulate the distributive behaviors of multiple agents. We further analyze the cooperative and

RAFF

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砍伐

INTER

AlphaZerolgorithm that has achieved superhuman performance in many challenging games. This chapter is divided into three parts: the first part introduces the concept of combinatorial games, the second part introduces the family of algorithms known as Monte Carlo Tree Search, and the third part takes Gomoku a

治愈

Robot Learning in Simulationrasping in CoppeliaSim and the deep reinforcement learning solution with soft actor-critic algorithm. The effects of different reward functions are also shown in the experimental sections, which testifies the importance of auxiliary dense rewards for solving a hard-to-explore task like the robot gra

admission

Parley

Theo Schiller,Petra Paulus,Andreas Klages present the integration architecture combining learning and planning, with detailed illustration on Dyna-Q algorithm. Finally, for the integration of learning and planning, the simulation-based search applications are analyzed.

眉毛

詩集

Karl-Rudolf Korte,Werner Weidenfeldoth continuous, which is a moderately large-scale environment for novices to gain some experiences. We provide a soft actor-critic solution for the task, as well as some tricks applied for boosting performances. The environment and code are available at ..