Decision Transformer casts RL as autoregressive sequence modeling conditioned on desired returns, past states and actions, matching or exceeding offline RL baselines on Atari, Gym and Key-to-Door tasks.
Explainable reinforcement learning through a causal lens
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Causal Agent Replay attributes LLM agent failures via do-interventions on step-level structural causal models, using contrastive estimators and Monte-Carlo Shapley values validated on synthetic ground truth.
A review that organizes causal decision making into three stages and consolidates methods into an open Python collection.
citing papers explorer
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Decision Transformer: Reinforcement Learning via Sequence Modeling
Decision Transformer casts RL as autoregressive sequence modeling conditioned on desired returns, past states and actions, matching or exceeding offline RL baselines on Atari, Gym and Key-to-Door tasks.
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Causal Agent Replay: Counterfactual Attribution for LLM-Agent Failures
Causal Agent Replay attributes LLM agent failures via do-interventions on step-level structural causal models, using contrastive estimators and Monte-Carlo Shapley values validated on synthetic ground truth.
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A Review of Causal Decision Making
A review that organizes causal decision making into three stages and consolidates methods into an open Python collection.