Multi-agent RL — what makes it fundamentally different?
hardAnswer
- (1) Environment now non-stationary from each agent's perspective (other agents' policies change during learning) — breaks Markov assumption.
- (2) Emergent behaviors (cooperation, competition, mixed-motive).
- (3) Credit assignment across agents in cooperative settings.
- (4) Coordination + equilibrium concepts (Nash, correlated).
- (5) Scale: joint action space grows exponentially.
- Foundational field for game theory + RL.
Check yourself — multiple choice
- Same as single
- Non-stationary env (others' policies change) + emergent cooperation/competition + credit assignment across agents + equilibrium concepts + exponential action space
- Random
- Only cooperation
Multi-agent RL: non-stationarity + coordination + equilibrium + credit + scale.
#multi-agent
Practise Reinforcement Learning
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