Artificial intelligence (AI) has enabled agents to master complex video games, from first-person shooters likeCounter-Striketo real-time strategy games such asStarCraft IIand racing games likeGran Turismo. While these achievements are notable, applying these AI methods in commercial video game production remains challenging due to computational constraints. In commercial scenarios, the majority of computational resources are allocated to 3D rendering, leaving limited capacity for AI methods, which often demand high computational power, particularly those relying on pixel-based sensors. Moreover, the gaming industry prioritizes creating human-like behavior in AI agents to enhance player experience, unlike academic models that focus on maximizing game performance. This paper introduces a novel methodology for training neural networks via imitation learning to play a complex, commercial-standard, VALORANT-like 2v2 tactical shooter game, requiring only modest CPU hardware during inference. Our approach leverages an innovative, pixel-free perception architecture using a small set of ray-cast sensors, which capture essential spatial information efficiently. These sensors allow AI to perform competently without the computational overhead of traditional methods. Models are trained to mimic human behavior using supervised learning on human trajectory data, resulting in realistic and engaging AI agents. Human evaluation tests confirm that our AI agents provide human-like gameplay experiences while operating efficiently under computational constraints. This offers a significant advancement in AI model development for tactical shooter games and possibly other genres.

Fictitious play is an algorithm for computing Nash equilibria of matrix games. Recently, machine learning variants of fictitious play have been successfully applied to complicated real-world games. This paper presents a simple modification of fictitious play which is a strict improvement over the original: it has the same theoretical worst-case convergence rate, is equally applicable in a machine learning context, and enjoys superior empirical performance. We conduct an extensive comparison of our algorithm with fictitious play, proving an optimal O(1/t) convergence rate for certain classes of games, demonstrating superior performance numerically across a variety of games, and concluding with experiments that extend these algorithms to the setting of deep multiagent reinforcement learning.

We examine 3-D wavelet coding of video with arbitrary regions of support (AROS). A critically sampled wavelet transform is applied to the AROS and a modified 3-D set partitioning in hierarchical trees (SPIHT) algorithm is used to quantize and code the wavelet coefficients in the AROS only. Experiments show that, for typical MPEG-4 pre-segmented sequences, our proposed method can achieve a gain of up to 5.6 dB in average PSNR at the same rate over 3-D SPIHT coding of regular volumes that embed the AROS of the given video sequences.

We consider the problem of error control for receiver-driven layered multicast of audio and video over the Internet. The sender injects into the network multiple source layers and multiple channel coding (parity) layers, some of which are delayed relative to the source, Each receiver subscribes to the number of source layers and the number of parity layers that optimizes the receiver's quality for its available bandwidth and packet loss probability. We augment this layered FEC system with layered pseudo-ARQ. Although feedback is normally problematic in broadcast situations, ARQ can be simulated by having the receivers subscribe and unsubscribe to the delayed parity layers to receive missing information. This pseudo-ARQ scheme avoids an implosion of repeat requests at the sender and is scalable to an unlimited number of receivers, We show gains of 4-18 dB on channels with 20% loss over systems without error control and additional gains of 1-13 dB when FEC is augmented by pseudo-ARQ in a hybrid system, Optimal error control in the hybrid system is achieved by an optimal policy for a Markov decision process.

We consider the problem of joint source/channel coding of real-time sources, such as audio and video, for the purpose of multicasting over the Internet. The sender injects into the network multiple source layers and multiple channel (parity) layers, some of which are delayed relative to the source. Each receiver subscribes to the number of source layers and the number of channel layers that optimizes the source-channel rate allocation for that receiver's available bandwidth and packet loss probability. We augment this layered FEC system with layered ARQ. Although feedback is normally problematic in broadcast situations, ARQ is simulated by having the receivers subscribe and unsubscribe to the delayed channel coding layers to receive missing information. This pseudo-ARQ scheme avoids an implosion of repeat requests at the sender, and is scalable to an unlimited number of receivers. We show gains of up to 18 dB on channels with 20% loss over systems without error control, and additional gains of up to 13 dB when FEC is augmented by pseudo-ARQ in a hybrid system. The hybrid system is controlled by an optimal policy for a Markov decision process.

This paper presents a POCS-based algorithm for consistent reconstruction of a signal x/spl isin/R/sup K/ from any subset of quantized coefficients y/spl epsiv/R/sup N/ in an N/spl times/K overcomplete frame expansion y=Fx, N=2K. By choosing the frame operator F to be the concatenation of two K/spl times/K invertible transforms, the projections may be computed in R/sup K/ using only the transforms and their inverses, rather than in the larger space R/sup N/ using the pseudo-inverse as proposed in earlier work. This enables practical reconstructions from overcomplete frame expansions based on wavelet, subband, or lapped transforms of an entire image, which has heretofore not been possible.

Three-dimensional (2D+T) wavelet coding of video using SPIHT has been shown to outperform standard predictive video coders on complex high-motion sequences, and is competitive with standard predictive video coders on simple low-motion sequences. However, on a number of typical moderate-motion sequences characterized by largely rigid motions, 3D SPIHT performs several dB worse than motion-compensated predictive coders, because it is does not take advantage of the real physical motion underlying the scene. We introduce global motion compensation for 3D subband video coders, and find 0.5 to 2 dB gain on sequences with dominant background motion. Our approach is a hybrid of video coding based on sprites, or mosaics, and subband coding.