Agents
Language Models can Solve Computer Tasks
However, previous approaches to this problem require large amounts of expert demonstrations and task-specific reward functions, both of which are impractical for new tasks.
Multi-Agent First Order Constrained Optimization in Policy Space
In the realm of multi-agent reinforcement learning (MARL), achieving high performance is crucial for a successful multi-agent system.
LightZero: A Unified Benchmark for Monte Carlo Tree Search in General Sequential Decision Scenarios
General decision intelligence needs to solve tasks in many distinct domains.
SMACv2: An Improved Benchmark for Cooperative Multi-Agent Reinforcement Learning Benjamin Ellis 1 Jonathan Cook
We show that these changes ensure the benchmark requires the use of closed-loop policies.
Multi-Player Zero-Sum Markov Games with Networked Separable Interactions Chanwoo Park
CCE collapses to the NE in such games when the payoffs are zero-sum.
