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BabyAI: A Platform to Study the Sample Efficiency of Grounded Language Learning
Maxime Chevalier-Boisvert, Dzmitry Bahdanau, Salem Lahlou, Lucas Willems, Chitwan Saharia, Thien Huu Nguyen, Yoshua Bengio
TL;DR
Human-in-the-loop grounded language learning is limited by the data demands of current methods. The paper introduces BabyAI, an extensible platform with 19 compositional-language levels and a heuristic teacher bot, and finds that realistic human teaching requires at least three orders of magnitude better sample efficiency.
Problem
Current imitation and reinforcement learning methods require enormous amounts of data, making human-machine teaching interactions costly.
Method
The paper introduces BabyAI, an extensible 19-level gridworld platform using compositional Baby Language and a heuristic bot to simulate a human teacher.
Results
Current imitation and reinforcement learning methods scale and generalize poorly on compositional tasks, requiring hundreds of thousands of demonstrations for tasks that seem trivial to humans.
Takeaways & Limitations
Curriculum learning and interactive learning provide measurable sample-efficiency improvements, but realistic human-in-the-loop training remains out of reach without at least three orders of magnitude further improvement.
Takeaways & Limitations
The experiments require between 20 and 50 GPUs for two weeks, with at least as much computation used for preliminary investigations.
Abstract
from arXiv · showhide
Allowing humans to interactively train artificial agents to understand language instructions is desirable for both practical and scientific reasons, but given the poor data efficiency of the current learning methods, this goal may require substantial research efforts. Here, we introduce the BabyAI research platform to support investigations towards including humans in the loop for grounded language learning. The BabyAI platform comprises an extensible suite of 19 levels of increasing difficulty. The levels gradually lead the agent towards acquiring a combinatorially rich synthetic language which is a proper subset of English. The platform also provides a heuristic expert agent for the purpose of simulating a human teacher. We report baseline results and estimate the amount of human involvement that would be required to train a neural network-based agent on some of the BabyAI levels. We put forward strong evidence that current deep learning methods are not yet sufficiently sample efficient when it comes to learning a language with compositional properties.
1 INTRODUCTION
BabyAI targets grounded language learning with humans in the loop by providing an extensible platform for studying how agents acquire instructions efficiently. The paper focuses on the data and interaction demands that currently make human teaching difficult.
- Platform and motivation: BabyAI is introduced as a research platform for grounded language learning with a simulated human expert standing in for a real teacher.Its goal is to support progress toward training agents with actual human involvement.
- Platform and motivation: The platform contains 19 increasingly difficult levels designed to support curriculum learning and interactive teaching.The levels gradually increase environment and language complexity.
- Sample-efficiency problem: The central obstacle is the large amount of data, and therefore human-machine interaction, required by current learning methods.The authors study demonstrations and episodes needed by imitation and reinforcement learning baselines.
- Contributions: The paper contributes baseline sample-efficiency results across BabyAI levels and investigates pretraining and interactive imitation learning as possible improvements.The platform and pretrained models are made available online.
2 RELATED WORK
BabyAI builds on synthetic-language and embodied-learning environments while combining partial observability, world-state manipulation, and a simulated human-in-the-loop setting. It uses a controlled synthetic language to measure how much data general-purpose agents need for near-perfect performance.
- Environment design: Unlike several prior environments, BabyAI supports both partial observability and manipulation of world state by moving objects.These features extend settings where agents could navigate but could not alter their surroundings.
- Environment design: BabyAI uses a 2D gridworld because more realistic 3D environments impose high computational costs for sample-efficiency studies.The platform implements its own MiniGrid environment because available gridworlds were insufficient for compositional language.
- Human-in-the-loop learning: Human-in-the-loop settings make reward queries expensive, motivating imitation learning methods that extract more learning from each human input.The comparison is framed around the cost of obtaining human-provided rewards or demonstrations.
- Evaluation focus: BabyAI differs from fixed-dataset reasoning benchmarks by measuring how much data a general-purpose model requires to approach perfect performance.Its focus is grounded language with a simulated human in the loop.
- Language design: The platform uses a synthetic language rather than natural language to control instruction semantics and generate data as needed.This supports systematic study of compositional instruction following.
3 BABYAI PLATFORM
BabyAI combines MiniGrid, Baby Language, a verifier, 19 competency-based levels, and a heuristic bot that simulates a human teacher. The platform supports compositional instructions whose execution may require navigation, manipulation, exploration, and implicit subgoals.
- Platform components: BabyAI comprises an efficiently simulated partially observable gridworld, instruction-following levels, a synthetic Baby Language, and a demonstration-generating bot.The code is open source and the environment integrates with the platform’s learning tasks.
- Baby Language: Baby Language is a formally structured, combinatorially rich subset of English containing 2.48 × 10^19 possible instructions.It expresses actions such as going to, picking up, opening doors, and placing objects next to one another.
- Baby Language: The language supports sequencing with “then” and “after” and conjunction with “and,” while descriptors identify objects by attributes and relative location.The grammar productions and examples illustrate compositional instruction structure.
- Verification and execution: A verifier checks whether an action sequence achieves an instruction’s goal, including cases where any matching object can satisfy a descriptor.Instructions may leave execution details implicit, such as finding keys or moving obstacles.
- BabyAI levels: The 19 levels are distributions of missions organized by required competencies and increasing environment complexity, culminating in a level requiring all competencies.Competencies include navigation, distractor handling, maze traversal, unblocking, unlocking, and language skills such as open, pickup, put, location, and sequences.
- The bot agent: The bot acts as a simulated human teacher by using task knowledge, instruction trees, and a stack machine to generate demonstrations or suggest actions.Its subgoals include opening, closing, picking up, dropping, going next to objects, and exploring unseen areas.
4 EXPERIMENTS
The experiments establish behavioral-cloning baselines, estimate imitation- and reinforcement-learning sample efficiency, and test pretraining, adaptive interaction, and demonstration-source effects. Results show that reinforcement learning is substantially less sample efficient than imitation learning, while suitable pretraining and interactive teaching can reduce demonstration requirements.
- 4.1 SETUP: The experiments evaluate behavioral cloning, imitation learning, reinforcement learning, curriculum pretraining, and interactive teaching on BabyAI levels.The study uses symbolic partial observations and variable-length instructions, with GRU- and convolutional-network-based models, Adam optimization, and PPO for reinforcement learning.
- 4.2 BASELINE RESULTS: 1M demonstrations per level provide baseline imitation-learning results, with all single-room levels reaching 100.0% validation success.The maximum success rate is measured on a validation set of 512 episodes; longer demonstrations generally correspond to more difficult levels.
- 4.2 BASELINE RESULTS: RL requires 2 to 10 times more episodes than IL in these experiments to reach the defined 99% success threshold.Table 3 reports IL 99% credible intervals and RL 99% confidence intervals for the required demonstrations or episodes.
- 4.2 BASELINE RESULTS: RL-generated demonstrations are easier to imitate than bot demonstrations, with less than 2K versus more than 8K required for GoToRedBallGrey.For GoToRedBall and GoToLocal, RL demonstrations yield 1.5-2 times better sample efficiency, which the authors relate to shared learner and expert architectures.
- 4.3 CURRICULUM LEARNING: Pretraining with GoToLocal reduces target-level demonstrations in four tested cases, whereas GoToObjMaze alone does not provide a benefit.The experiments show that the choice of base level is crucial for whether pretraining helps.
- 4.4 INTERACTIVE LEARNING: Interactive imitation reduces demonstrations substantially across all three tested levels, by 4 times for GoToRedBallGrey and 1.5-2 times for the other two.The protocol iteratively adds demonstrations for missions on which the agent failed until the estimated requirement for 99% success is reached.
5 CONCLUSION & FUTURE WORK
BabyAI is an open-source, extensible platform for studying human-in-the-loop grounded language learning through 19 increasingly difficult levels. Its results indicate that current methods remain far from the sample efficiency needed for realistic human teaching, motivating new models and teaching strategies.
- BabyAI comprises 19 levels of increasing difficulty based on basic competencies and requires learning Baby Language, a compositional subset of English.The platform is open source and allows new levels and language concepts to be integrated easily.
- Current imitation learning and reinforcement learning methods scale and generalize poorly on tasks with compositional structure.The reported results suggest that tasks appearing trivial to humans can remain difficult for current learning systems.
- Hundreds of thousands of demonstrations are needed to learn tasks that seem trivial by human standards.
- Curriculum learning and interactive learning measurably improve sample efficiency, but realistic human-in-the-loop training requires at least three orders of magnitude more improvement.
- Future work should seek better sample-efficiency strategies through new models and teaching methods, including approaches with explicit modularity and subroutines.
A.1 REINFORCEMENT LEARNING
The reinforcement-learning evaluation estimates how many training episodes are needed to reach a high success-rate threshold. It uses repeated experiments and reports uncertainty around the resulting episode counts.
- RL sample requirements are estimated by recording when smoothed online success crosses the 99% success-rate threshold.
- Each experiment is repeated 10 times, and the estimates are reported with a 99% t-test confidence interval.
A.2 IMITATION LEARNING
The imitation-learning protocol estimates the minimum demonstration count needed to reach 99% average success while reducing computational cost through early stopping and Gaussian-process interpolation. The estimate is approximate because the modeled posterior need not be monotonic.
- Estimating the demonstration count for a target imitation-learning performance is computationally expensive because each dataset size would require full model convergence.
- A semi-automatic protocol makes estimation practical by combining early stopping with nonparametric interpolation between observed data points.
- Early Stopping Using Normal Time: Early stopping uses an estimated normal training time from three models trained with 10^6 demonstrations, then stops other runs using different demonstration counts.
- Interpolation Using Gaussian Processes: A Gaussian Process interpolates success rate as a function of demonstration count and estimates the minimum samples needed for 99% average success.The GP provides uncertainty-aware predictions between observed data points.
- The posterior approximation is limited because the modeled average success curve is not necessarily monotonic, although the observed data shaped it toward monotonicity in practice.
B.2 REWARD FUNCTION
MiniGrid uses sparse rewards and a fixed episode time limit. Successful episodes receive a reward that decreases with the number of steps taken, while unsuccessful episodes receive zero.
- The agent receives positive reward only when it satisfies the environment’s success criterion within the time-step limit; otherwise, the reward is zero.
- Successful-episode rewards follow 1 − 0.9 ∗ (step_count/max_steps), so quicker solutions receive rewards closer to 1.
- MiniGrid provides seven actions, including movement, object pickup and dropping, door toggling, rotation, and episode completion.
B.4 OBSERVATION SPACE
MiniGrid provides partial, egocentric observations encoded compactly as a 7x7x3 tensor rather than RGB images. RGB views are supplied for human viewing.
- Observation space: The agent sees a partial, egocentric square of 7x7 tiles oriented in its facing direction, including its current tile.Walls and closed doors block visibility.
- Observation space: The observation tensor has shape 7x7x3 and is not an RGB image.This compact representation was selected for space efficiency and faster training.
- Observation space: Each tile is represented by three integer values encoding its object type, color, and door state.Door states distinguish open, closed, and locked doors.
- Observation space: Fully observable RGB environment views are provided for human viewing.
C BOT IMPLEMENTATION DETAILS
The bot decomposes instructions into stacked subgoals and processes them independently, producing further subgoals or environment actions. Action execution updates the bot's state and visibility mask, while specific subgoal procedures are illustrated separately.
- Instruction processing: Instructions are translated into an initial stack of subgoals, ordered bottom to top by execution priority.The lowest subgoal in the illustration is executed first.
- Instruction processing: Each subgoal is processed independently and either adds more subgoals to the stack or triggers an action.
- State and visibility: After an action changes the environment state, the bot updates its visibility mask with newly observed cells and objects.The visibility mask is used when searching for objects and paths.
- Subgoal handling: Close, Drop, and Pickup directly execute their corresponding actions and then remove themselves from the stack.
- Subgoal handling: Figures 5, 6, and 7 depict how the bot processes the Open, GoNextTo, and Explore subgoals, respectively.The implementation defines a forward cell as the grid cell the agent faces.