This paper presents an Exploratory 3D Dance generation framework, E3D2, designed to address the exploration capability deficiency in existing music-conditioned 3D dance generation models. Current models often generate monotonous and simplistic dance sequences that misalign with human preferences because they lack exploration capabilities.The E3D2 framework involves a reward model trained from automatically-ranked dance demonstrations, which then guides the reinforcement learning process. This approach encourages the agent to explore and generate high quality and diverse dance movement sequences. The soundness of the reward model is both theoretically and experimentally validated. Empirical experiments demonstrate the effectiveness of E3D2 on the AIST++ dataset.

This article offers an integrated solution for first-person shooter (FPS) games to train agents with adaptive strategies. Solving such complex decision tasks requires generalization ability and adaptive strategies. We develop a framework using a novel adaptive strategic control algorithm combined with advanced techniques, such as the hindsight experience replay, multiagent reinforcement learning, and league training. The approach adopts a multistage learning scheme, consisting of learning a goal-conditioned navigation policy, then transferring to learn sophisticated shooting skills by playing against a league of players, and finally learning adaptive strategies. Our agent achieves the SOTA result in pastViZDoomAI Competitions, surpassing previous top-ranked agents (never seen during training) by a large margin. We provide comprehensive analysis and experiments to elaborate the effect of each component in affecting the agent performance and demonstrate that the proposed and adopted techniques are essential to achieve superior performance inViZDoomCompetition and potentially valuable for general end-to-end FPS games.

Graph neural networks, which typically exchange information between local neighbors, often struggle to capture long-range interactions (LRIs) within the graph. Building a graph hierarchy via graph pooling methods is a promising approach to address this challenge; however, hierarchical information propagation cannot entirely take over the role of local information aggregation. To balance locality and hierarchy, we integrate the local and hierarchical structures, represented by intra- and inter-graphs respectively, of a multi-scale graph hierarchy into a single mega graph. Our proposed MeGraph model consists of multiple layers alternating between local and hierarchical information aggregation on the mega graph. Each layer first performs local-aware message-passing on graphs of varied scales via the intra-graph edges, then fuses information across the entire hierarchy along the bidirectional pathways formed by inter-graph edges. By repeating this fusion process, local and hierarchical information could intertwine and complement each other. To evaluate our model, we establish a new Graph Theory Benchmark designed to assess LRI capture ability, in which MeGraph demonstrates dominant performance. Furthermore, MeGraph exhibits superior or equivalent performance to state-of-the-art models on the Long Range Graph Benchmark. The experimental results on commonly adopted real-world datasets further demonstrate the broad applicability of MeGraph.