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Causal Agents and Reinforcement Learning
Causal Decision Theory
Decision Theory Primer
Decision Theory is a Causal Problem
Decision Rules: Argmax, Minimax, and Softmax
Statistical Hypothesis Testing, Bayes Rules, and Admissibility
Causality and Sequential Decision Processes
Reaction, Deliberation, Intention & Free Will
Sequential Decision Process as Causal DAGs
Intro to Markov Decision Processes
Markov Decision Processes as a Causal DAG
Policies and Interventions
The Bellman Equation in Causal Terms
Transition Functions as Structural Causal Models
Modeling Agents with Causal Probabilistic Programming
MDPs as Probabilistic Programs
Programming Policy as a Do-Operator
Context and the Do-Operator: Epidemic Example
The Do-Op for Introspecting Agents
Planning as Inference: One-shot Policies
Planning as Inference: Programming MDPs
Causal Reinforcement Learning
Bandit Algorithms 101 (with Causal DAGs)
Bayesian Bandits and Bayesian Thompson Sampling
Causal Bandit Algorithms
The Bellman Equation in Causal Terms
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