Research map / Multi-agent systems and games / Human-AI and goal-conditioned
Been There, Done That: Meta-Learning with Episodic Recall
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add context-keyed episodic memory with gated cell-state reinstatement | L2RL · Barcode bandits | reward | Helped |
| Add context-keyed episodic memory with gated cell-state reinstatement | L2RL · Omniglot bandits | regret | Helped |
| Add context-keyed episodic memory with gated cell-state reinstatement | L2RL · Compositional bandits | regret | Helped |
| Add context-keyed episodic memory with gated cell-state reinstatement | L2RL · Contextual water maze | steps to reach goal | Helped |
| Add context-keyed episodic memory with gated cell-state reinstatement | L2RL · Episodic two-step task | reward | Helped |
| Feed retrieved DND states as LSTM inputs instead of adding them through the reinstatement gate (ablation) | epL2RL · Barcode bandits | reward | Hurt |
| Compress retrieved states with linear layers or MLPs rather than using gated reinstatement (ablation) | epL2RL · bandit tasks | reward | Mixed |
| Provide context embeddings as LSTM inputs | L2RL · Omniglot bandits | reward | Hurt |
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