Cooperative Multi-Agent RL
Advanced
Multi-agent RL: N agents cooperate to maximize shared reward. Each agent observes partial state, must coordinate via learned behavior or communication.
The problem, from first principles
Multiple agents learning simultaneously create non-stationary environment. Agents must learn to cooperate despite seeing only partial observations. Scalability challenge.
From theory to code
Implement compute_cooperative_value(individual_values, reward_scale) which:
- Takes individual agent value estimates
- Combines into cooperative objective
- Values shared reward: combined_value = sum(individual_values) + reward_scale * shared_reward
- Returns total cooperative value
Constraints
- Partial observability: each agent sees subset of state
- Non-stationary: other agents' policies changing
- Scalability: learning degrades with more agents
- Communication may help but adds complexity
Hints
Hint 1: Shared reward
All agents get same reward signal for cooperative successHint 2: Credit assignment
Difficult to assign credit: which agent caused success?Hint 3: QMIX/MADDPG
Modern approaches: value decomposition or centralized trainingKeywords
rlmulti-agentcooperationcommunication
Python
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