Grandmaster level in StarCraft II using multi-agent reinforcement learning.

Many real-world applications require artificial agents to compete and coordinate with other agents in complex environments. As a stepping stone to this goal, the domain of StarCraft has emerged as an important challenge for artificial intelligence research, owing to its iconic and enduring status among the most difficult professional esports and its relevance to the real world in terms of its raw complexity and multi-agent challenges. Over the course of a decade and numerous competitions1-3, the strongest agents have simplified important aspects of the game, utilized superhuman capabilities, or employed hand-crafted sub-systems4. Despite these advantages, no previous agent has come close to matching the overall skill of top StarCraft players. We chose to address the challenge of StarCraft using general-purpose learning methods that are in principle applicable to other complex domains: a multi-agent reinforcement learning algorithm that uses data from both human and agent games within a diverse league of continually adapting strategies and counter-strategies, each represented by deep neural networks5,6. We evaluated our agent, AlphaStar, in the full game of StarCraft II, through a series of online games against human players. AlphaStar was rated at Grandmaster level for all three StarCraft races and above 99.8% of officially ranked human players.

Atomic-layer electroless deposition: a scalable approach to surface-modified metal powders.

Palladium has a number of important applications in energy and catalysis in which there is evidence that surface modification leads to enhanced properties. A strategy for preparing such materials is needed that combines the properties of (i) scalability (especially on high-surface-area substrates, e.g. powders); (ii) uniform deposition, even on substrates with complex, three-dimensional features; and (iii) low-temperature processing conditions that preserve nanopores and other nanostructures. Presented herein is a method that exhibits these properties and makes use of benign reagents without the use of specialized equipment. By exposing Pd powder to dilute hydrogen in nitrogen gas, sacrificial surface PdH is formed along with a controlled amount of dilute interstitial hydride. The lattice expansion that occurs in Pd under higher H2 partial pressures is avoided. Once the flow of reagent gas is terminated, addition of metal salts facilitates controlled, electroless deposition of an overlayer of subnanometer thickness. This process can be cycled to create thicker layers. The approach is carried out under ambient processing conditions, which is an advantage over some forms of atomic layer deposition. The hydride-mediated reaction is electroless in that it has no need for connection to an external source of electrical current and is thus amenable to deposition on high-surface-area substrates having rich, nanoscale topography as well as on insulator-supported catalyst particles. STEM-EDS measurements show that conformal Rh and Pt surface layers can be formed on Pd powder with this method. A growth model based on energy-resolved XPS depth profiling of Rh-modified Pd powder is in general agreement. After two cycles, deposits are consistent with 70-80% coverage and a surface layer with a thickness from 4 to 8 Å.