SPOTting the Future: Lookahead Explanations for Deep Reinforcement Learning
This episode discusses 'SPOT' (Sampling Policy Observation Tree), a method for explaining Deep Reinforcement Learning agents. Unlike methods that only look at current features, SPOT simulates potential future actions to build a tree of outcomes. This allows human operators to see the long-term consequences of an agent's decision, providing actionable insights like TRUST or INTERVENE rather than just looking at the present.