Agents
Zero-sum Polymatrix Markov Games: Equilibrium Collapse and Efficient Computation of Nash Equilibria
One such example is two-player zero-sum Markov games, in which efficient ways to compute a Nash equilibrium are known. Inspired by zero-sum polymatrix normal-form games (Cai et al., 2016), we define a class of zero-sum
AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents
AI agents aim to solve complex tasks by combining text-based reasoning with external tool calls.
Learning from Visual Observation via Offline Pretrained State-to-Go Transformer Bohan Zhou 1 Ke Li
Learning from visual observation (LfVO), aiming at recovering policies from only visual observation data, is promising yet a challenging problem.
Selectively Sharing Experiences Improves Multi-Agent Reinforcement Learning
We present a novel multi-agent RL approach, Selective Multi-Agent Prioritized Experience Relay, in which agents share with other agents a limited number of transitions they observe during training.
The Waymo Open Sim Agents Challenge
Simulation agents are controlled objects that perform realistic behaviors in a virtual world.
