Editing Multi-Agent Reinforcement Learning (section)

== <span style="color: #FFFFFF;">Remembering</span> ==
* '''Multi-agent system''' — A system composed of multiple interacting autonomous agents, each making decisions in a shared environment.
* '''Cooperative MARL''' — All agents share a common reward and must coordinate to maximize it collectively.
* '''Competitive MARL''' — Agents have opposing objectives; one agent's gain is another's loss (zero-sum games).
* '''Mixed (general-sum) MARL''' — Agents have partially aligned and partially conflicting objectives.
* '''Centralized training, decentralized execution (CTDE)''' — The dominant MARL paradigm: train agents with access to global information, but at deployment each agent acts only on local observations.
* '''Joint action space''' — The combined action space of all agents; grows exponentially with the number of agents.
* '''Partial observability''' — Each agent observes only part of the global state; the Dec-POMDP framework models this.
* '''Non-stationarity''' — From any agent's perspective, the environment is non-stationary because other agents are also learning and changing their policies.
* '''Value decomposition''' — Methods that decompose a joint value function into per-agent components for scalable learning (VDN, QMIX).
* '''QMIX''' — A cooperative MARL algorithm that learns a monotonic mixing function over individual agent Q-values.
* '''MADDPG (Multi-Agent Deep Deterministic Policy Gradient)''' — A CTDE actor-critic method for continuous action spaces in multi-agent settings.
* '''Communication in MARL''' — Allowing agents to send messages to each other, improving coordination.
* '''Emergent behavior''' — Complex collective behaviors arising from individual agent policies without explicit programming.
* '''Mean-field game''' — An approximation for very large agent populations where each agent interacts with the mean behavior of others.
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