Multi-Agent RL
Self-play, equilibria, credit assignment across agents, emergent coordination and non-stationarity.
5concepts
60flashcards
35minutes of reading
- 01 Credit Assignment Across Agents Why a shared team reward gives every agent the same noisy signal, how counterfactual baselines and value factorisation isolate individual contributions, and the structural assumption each makes.
- 02 Emergent Coordination and Communication How agents develop coordinated behaviour without being told to, why learned communication protocols are hard to train and harder to interpret, and what counts as evidence of genuine communication.
- 03 Equilibria as Learning Targets Why Nash equilibrium is the wrong objective for most multi-agent learning, what the alternatives assume, and how the game's structure determines whether convergence is even possible.
- 04 Non-Stationarity in Multi-Agent Learning Why every guarantee from single-agent RL dissolves when other learners are present, what breaks in the Markov assumption, and the two architectural responses.
- 05 Self-Play and Population-Based Training Why playing against yourself generates an automatic curriculum, the cycling and forgetting failures that follow, and how a league of opponents fixes both.