Papers I Read Notes and Summaries

BabyAI - First Steps Towards Grounded Language Learning With a Human In the Loop


  • BabyAI is a research platform to investigate and support the feasibility of including humans in the loop for grounded language learning.

  • The setup is a series of levels (of increasing difficulty) to train the agent to acquire a synthetic language (Baby Language) which is a proper subset of English language.

  • Link to the paper


  • BabyAI platform provides support for curriculum learning and interactive learning as part of its human-in-the-loop training setup.

  • Curriculum learning is incorporated by having a curriculum of levels of increasing difficulty.

  • Interactive learning is supported by including a heuristic expert which can provide new demonstrations on the fly to the learning agent.

  • The heuristic expert can be thought of as the human-in-the-loop which can guide the agent through the learning process.

  • One downside of human-in-the-loop is the poor sample complexity of the learning agent. The heuristic agent can be used to estimate the sample efficiency.


  • BabyAI research platform for grounded language learning with a simulated human-in-the-loop.

  • Baseline results for performance and sample efficiency for the different tasks.

BabyAI Platform


  • MiniGrid - A partially observable 2D grid-world environment.

  • Entities - Agent, ball, box, door, keys

  • Actions - pick, drop or move objects, unlock doors etc.

Baby Language

  • Synthetic Language (a proper subset of English) - Used to give instructions to the agent

  • Support for verifying if the task (and the subtasks) are completed or not


  • A level is an instruction-following task.

  • Formally, a level is a distribution of missions - a combination of initial state of the environment and an instruction (in Baby Language)

  • Motivated by curriculum learning, the authors create a series of tasks (with increasing difficulty).

  • A subset of skills (competencies) is required for solving each task. The platform takes into account this constraint when creating a level.

Heuristic Expert

  • The platform supports a Heuristic expert that simulates the role of a human teacher and knows how to solve each task.

  • For any level, it can suggest actions or generate demonstrations (given the state of the environment).


  • An imitation learning baseline is trained for each level.

  • Data requirement for each level and the benefits of curriculum learning and imitation learning are investigated (in terms of sample efficiency).

Model Architecture

  • GRU to encode the sentence, CNN to encode the input observation

  • FiLM layer to combine the two representations

  • LSTM to encode the per-timestep FiLM encoding (timesteps in the environment)

  • Two model variants are considered:

    • Large Model - Bidirectional GRU + attention + large hidden state

    • Small Model - Unidirectional GRU + No attention + small hidden state

  • Heuristic expert used to generate trajectory and the models are trained by imitation learning (to be used as baselines)


  • The key takeaway is that the current deep learning approaches are extremely sample inefficient when learning a compositional language.

  • Data efficiency of RL methods is much worse than that of imitation learning methods showing that the current imitation learning and reinforcement learning methods scale and generalize poorly.

  • Curriculum-based pretraining and interactive learning was found to be useful in only some cases.