1. Introduction  
1.1. Significance of IRL  
1.2. Importance of this Survey  
1.3. Organization of Contents

2. Formal Definition of IRL

3. Primary Challenges of IRL  
3.1. Obstacles to Accurate Inference  
3.3. Sensitivity to Correctness of Prior Knowledge  
3.4. Disproportionate Growth in Solution Complexity with Problem Size  
3.5. Direct Learning of Reward or Policy Matching

4. Foundational Methods for IRL  
4.1. Margin Optimization  
4.2. Entropy Optimization  
4.3. Bayesian Update  
4.4. Classification and Regression  
4.5. Summary of the Methods

5. Mitigating the Challenges  
5.1. Improving the Accuracy of Inference  
5.2. Generalizability  
5.3. Lowering Sensitivity to Prior Knowledge  
5.4. Analysis and Reduction of Complexity  

6. Extensions of Basic IRL  
6.1. Incomplete and Imperfect Observations  
6.2. Multiple Tasks  
6.3. Incomplete Model

7. Concluding Remarks and Future Work