A survey on game playing agents and large models: Methods, applications, and challenges
The swift evolution of Large-scale Models (LMs), either language-focused or multi-modal,
has garnered extensive attention in both academy and industry. But despite the surge in …
has garnered extensive attention in both academy and industry. But despite the surge in …
Rl-vigen: A reinforcement learning benchmark for visual generalization
Abstract Visual Reinforcement Learning (Visual RL), coupled with high-dimensional
observations, has consistently confronted the long-standing challenge of out-of-distribution …
observations, has consistently confronted the long-standing challenge of out-of-distribution …
Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning
Numerous methods are proposed for the Traffic Signal Control (TSC) tasks aiming to provide
efficient transportation and mitigate congestion waste. In recent, promising results have …
efficient transportation and mitigate congestion waste. In recent, promising results have …
Games for Artificial Intelligence Research: A Review and Perspectives
Games have been the perfect test-beds for artificial intelligence research for the
characteristics that widely exist in real-world scenarios. Learning and optimisation, decision …
characteristics that widely exist in real-world scenarios. Learning and optimisation, decision …
Hokoff: real game dataset from honor of kings and its offline reinforcement learning benchmarks
Abstract The advancement of Offline Reinforcement Learning (RL) and Offline Multi-Agent
Reinforcement Learning (MARL) critically depends on the availability of high-quality, pre …
Reinforcement Learning (MARL) critically depends on the availability of high-quality, pre …
Revisiting discrete soft actor-critic
We study the adaption of soft actor-critic (SAC) from continuous action space to discrete
action space. We revisit vanilla SAC and provide an in-depth understanding of its Q value …
action space. We revisit vanilla SAC and provide an in-depth understanding of its Q value …
Tackling cooperative incompatibility for zero-shot human-ai coordination
Securing coordination between AI agent and teammates (human players or AI agents) in
contexts involving unfamiliar humans continues to pose a significant challenge in Zero-Shot …
contexts involving unfamiliar humans continues to pose a significant challenge in Zero-Shot …
Technical challenges of deploying reinforcement learning agents for game testing in aaa games
J Gillberg, J Bergdahl, A Sestini… - … IEEE Conference on …, 2023 - ieeexplore.ieee.org
Going from research to production, especially for large and complex software systems, is
fundamentally a hard problem. In large-scale game production, one of the main reasons is …
fundamentally a hard problem. In large-scale game production, one of the main reasons is …
Enhancing Human Experience in Human-Agent Collaboration: A Human-Centered Modeling Approach Based on Positive Human Gain
Existing game AI research mainly focuses on enhancing agents' abilities to win games, but
this does not inherently make humans have a better experience when collaborating with …
this does not inherently make humans have a better experience when collaborating with …
Probabilistic Offline Policy Ranking with Approximate Bayesian Computation
In practice, it is essential to compare and rank candidate policies offline before real-world
deployment for safety and reliability. Prior work seeks to solve this offline policy ranking …
deployment for safety and reliability. Prior work seeks to solve this offline policy ranking …