Behavioral Cloning (BC) is the simplest form of imitation learning — treating the expert's demonstrations as a supervised learning dataset and training a policy to predict the expert's actions from the observed states: $pi(a|s) approx pi_{expert}(a|s)$.

BC Details

• Dataset: Expert demonstrations ${(s_i, a_i)}$ — state-action pairs from an expert policy.
• Training: Supervised learning — minimize $L = sum_i |a_i - pi_ heta(s_i)|^2$ (regression) or cross-entropy (classification).
• Simple: Just a standard supervised learning problem — any neural network architecture works.
• Distribution Shift: At test time, small errors compound — the agent visits states not in the training data.

Why It Matters

• Simplicity: No reward function, no RL — just supervised learning on demonstrations.
• Compounding Errors: The main limitation — distributional shift causes errors to accumulate over time.
• Baseline: BC is the baseline for all imitation learning methods — if BC works well, more complex methods may not be needed.

BC is copy the expert — the simplest imitation learning approach, directly supervised on expert demonstrations.