Imitation Learning (IL) is a machine learning approach to learn a policy from a set of demonstrations. IL can be useful to kick-start learning before applying reinforcement learning (RL) but it can also be useful on its own, e.g. to learn to imitate human players in video games. However, a major limitation of current IL approaches is that they learn only a single "average" policy based on a dataset that possibly contains demonstrations of numerous different types of behaviors. In this paper, we present a new approach called Behavioral Repertoire Imitation Learning (BRIL) that instead learns a repertoire of behaviors from a set of demonstrations by augmenting the state-action pairs with behavioral descriptions. The outcome of this approach is a single neural network policy conditioned on a behavior description that can be precisely modulated. We apply this approach to train a policy on 7,777 human demonstrations for the build-order planning task in StarCraft II. Dimensionality reduction techniques are applied to construct a lowdimensional behavioral space from the high-dimensional army unit composition of each demonstration. The results demonstrate that the learned policy can be effectively manipulated to express distinct behaviors. Additionally, by applying the UCB1 algorithm, the policy can adapt its behavior -in-between games -to reach a performance beyond that of the traditional IL baseline approach.
Summary: 
The paper proposes behavioral repertoire imitation learning (BRIL) which aims to learn a collection of policy from diverse demonstrations. BRIL learns such a collection by learning a context-dependent policy, where the context variable represents behavior of each demonstration. To obtain a context variable, BRIL rely on user’s knowledge, where the user manually defines a feature space that describes behavior. This feature space is then reduced by using a dimensionality reduction method such as t-SNE. Lastly, the policy is learned by supervised learning (behavior cloning) with a state-context input variable and an action output variable. The method is experimentally evaluated on the StarCraft environment. The results show that BRIL performs better than two baselines: behavior cloning on diverse demonstrations and behavior cloning on clustered demonstrations. 
Score: 
The weaknesses of the paper are novelty, clarity, and evaluation. Please see the detailed comments below. I vote for rejection. 
Comments:
- Novelty of the proposed idea. 
The major issue of the paper is the lack of novelty. The idea of learning a context-dependent policy in BRIL closely resembles that of existing multi-modal IL methods (Wang et al., 2017, Li et al., 2017). The main difference is that, BRIL relies on a manually defined context variable (behavioral feature space). In contrast, the existing methods aim to learn the context variable from demonstrations. BRIL is too simple when compared to the existing methods. Moreover, using a manually specified feature space does not go well with the main principle of deep learning, which is to learn informative feature spaces from data end-to-end. I think that ICLR is not a suitable venue for this paper. 
- Clarity of the proposed method.
The second issue of the paper is clarity. Specifically, two important steps of BRIL is policy learning by supervised learning (behavior cloning) and dimensionality reduction by t-SNE. However, explanations of these two steps are vague and incomplete. For example, in Section 2.1, the paper describes IL as supervised learning, but does not mention the issue of covariate shift, which is well-known when treating IL as supervised learning (Ross et al., 2011). Also, it is incorrect to state that an IL agent cannot interact with the environment during training, since many IL methods such as GAIL require interactions with the environment during training. Meanwhile, in Section 2.4, it is unclear how probability distributions in t-SNE reflect similarity between data points. 
[1] Stéphane Ross, Geoffrey Gordon, and Drew Bagnell. A reduction of imitation learning and structured prediction to no-regret online learning. AISTATS, 2011.
- Evaluation of the proposed method is too narrow.
The paper lacks important baseline methods in the experiment. Specifically, the paper does not compare BRIL against multi-modal IL methods (Wang et al., 2017, Li et al., 2017) which also learn a context-dependent policy. Moreover, BRIL is evaluated only on the StarCraft environment with only one kind of manually specified feature. This raises a question of generality and sensitivity against the choice of feature of BRIL. To improve the paper, I suggest the authors to compared the proposed method against multi-modal IL methods on different environment, and evaluate BRIL with different choices of the behavioral features.