Source-linked AI summary
Behavioral Cloning from Observation
Faraz Torabi, Garrett Warnell, Peter Stone
TL;DR
Imitation learning often assumes demonstrator actions and substantial post-demonstration interaction, unlike human imitation from observation. BCO learns an agent-specific inverse dynamics model before demonstrations, infers missing actions from state trajectories, and applies behavioral cloning, with iterative model improvement when interaction is available. Experiments report favorable policies relative to action-informed methods while requiring fewer post-demonstration interactions and less delay.
Problem
Existing LfD methods commonly require demonstrator actions and may require costly post-demonstration environment interaction, limiting imitation from state-only observations.
Method
BCO learns a task-independent, agent-specific inverse dynamics model before demonstrations, infers missing expert actions, and uses them for behavioral cloning; its iterative variant improves the model with post-demonstration data.
Results
BCO produces imitation policies that perform favorably relative to existing action-informed approaches while requiring fewer post-demonstration interactions.
Takeaways & Limitations
BCO provides reasonable imitation policies from state-only demonstrations with less delay and can trade post-demonstration interaction for imitation performance.
Takeaways & Limitations
Standard behavioral cloning remains applicable only when demonstrator action sequences are available, so it cannot directly handle the paper’s state-only setting.
Abstract
from arXiv · showhide
Humans often learn how to perform tasks via imitation: they observe others perform a task, and then very quickly infer the appropriate actions to take based on their observations. While extending this paradigm to autonomous agents is a well-studied problem in general, there are two particular aspects that have largely been overlooked: (1) that the learning is done from observation only (i.e., without explicit action information), and (2) that the learning is typically done very quickly. In this work, we propose a two-phase, autonomous imitation learning technique called behavioral cloning from observation (BCO), that aims to provide improved performance with respect to both of these aspects. First, we allow the agent to acquire experience in a self-supervised fashion. This experience is used to develop a model which is then utilized to learn a particular task by observing an expert perform that task without the knowledge of the specific actions taken. We experimentally compare BCO to imitation learning methods, including the state-of-the-art, generative adversarial imitation learning (GAIL) technique, and we show comparable task performance in several different simulation domains while exhibiting increased learning speed after expert trajectories become available.
1 Introduction
The paper targets imitation from observation without demonstrator actions and with little post-demonstration interaction. BCO addresses both through pre-demonstration model learning, action inference, and behavioral cloning.
- Motivation: LfD research has largely overlooked imitation from observation without demonstrator actions and rapid learning after demonstrations.Humans typically observe state trajectories rather than internal control signals and can imitate without extensive environmental interaction.
- Motivation: State-only demonstrations could expand LfD to sources such as tutorial videos that omit action sequences.The paper identifies these demonstrations as currently unusable by conventional action-conditioned methods.
- Motivation: Post-demonstration environment interaction can introduce delay, repeated costs, and risks such as damage during autonomous-vehicle training.The paper therefore seeks reusable pre-processing experience and minimal interaction after each demonstration.
- BCO: BCO first learns a task-independent inverse dynamics model, then infers missing expert actions from state trajectories and applies behavioral cloning.Its iterative variant reuses the updated policy to collect data and improve the model and imitation policy.
2 Related Work
Prior imitation-learning methods commonly require demonstrator actions, while BCO combines inverse dynamics modeling with imitation from state-only observations. Related work also frames model-based learning as more sample-efficient and transferable across tasks.
- Behavioral cloning: Behavioral cloning trains a classifier or regressor on demonstrator states and actions to replicate the expert policy immediately without environment interaction.Its central limitation in this setting is dependence on available demonstrator action sequences.
- Inverse reinforcement learning: Inverse reinforcement learning typically assumes state-action pairs and learns a cost function before using reinforcement learning to find an imitation policy.The cited exception is described as operating under different assumptions, but the passage does not detail them fully.
- Model-based learning: Model-based methods are presented as more sample-efficient than model-free methods and capable of transferring learned models across tasks.BCO is related to this literature because it uses learned environment models.
- Related approaches: Prior inverse-model approaches estimate missing actions, whereas BCO develops a method for imitation without action information and with limited post-demonstration interaction.The supplied passage identifies this distinction but truncates the detailed comparison.
3 Problem Formulation
The paper formulates imitation from observation as learning a good policy from state-only demonstrations while minimizing post-demonstration environment interactions. BCO separates this into inverse dynamics-model learning and imitation-policy learning.
- MDP setting: An MDP is represented by state and action spaces, transition dynamics, rewards, and a discount factor.Policies map states to actions or action distributions, and trajectories contain the transitions experienced under a policy.
- Inverse dynamics: The inverse dynamics model estimates the probability of an action given a transition from s_i to s_i+1.The paper seeks a task-independent model by separating agent-specific from task-specific state features.
- Imitation from observation: Imitation from observation uses state-only trajectories D = {ζ1, ζ2, ...} rather than demonstrator state-action pairs.Each trajectory consists of an ordered sequence of observed states.
- Objective: The central objective is to find a good imitation policy from state-only demonstrations using a minimal number of post-demonstration interactions, |Ipost|.Environment interactions are action executions that produce new observed data, with pre- and post-demonstration sets denoted Ipre and Ipost.
- Proposed framework: BCO addresses the problem with separate components for learning an agent-specific inverse dynamics model and learning an imitation policy from demonstrations.This decomposition supports operation without demonstrator action information and with no or very few post-demonstration interactions.
4 Behavioral Cloning from Observation
BCO learns an agent-specific inverse dynamics model before demonstration, infers missing demonstrator actions from state-only trajectories, and applies behavioral cloning to learn an imitation policy. An optional iterative variant uses post-demonstration interaction to improve the model and policy.
- 4.1 Inverse Dynamics Model Learning: BCO first learns an agent-specific inverse dynamics model from exploratory environment interactions before observing demonstrations.The model is trained on observed state transitions and associated actions, using maximum-likelihood estimation.
- 4.2 Behavioral Cloning: Given state-only demonstrations, BCO extracts agent-specific transitions and uses the learned model to infer the demonstrator’s missing actions.The model-predicted action distribution supplies maximum-likelihood inferred actions for the demonstrated transitions.
- 4.2 Behavioral Cloning: BCO learns the imitation policy by applying behavioral cloning to the demonstrated states paired with inferred actions.The policy parameters are fitted by maximum-likelihood estimation; continuous actions use Gaussian policies and discrete actions use softmax probabilities.
- 4.3 Model Improvement: The modified BCO(α) variant repeatedly executes the imitation policy after demonstration, updates the inverse dynamics model with new state-action sequences, and improves the policy until progress stops.The post-demonstration interaction amount is controlled by α, with M = α|Ipre| interactions per model-improvement iteration.
5 Implementation and Experimental Results
The experiments evaluate BCO across several domains for interaction efficiency and demonstration-data efficiency, comparing it with methods that use demonstrator actions. BCO achieves comparable performance in several settings while requiring substantially fewer post-demonstration interactions.
- Evaluation Setup: The evaluation covers CartPole, MountainCar, Reacher, and Ant-v1, ordered from easier to harder according to dynamics and state-action complexity.The study evaluates interaction requirements and task performance as functions of available demonstration data.
- Baselines: BCO is compared with Behavioral Cloning, Feature Expectation Matching, and Generative Adversarial Imitation Learning.BC uses demonstrator state-action pairs, while FEM and GAIL are alternative imitation-learning methods.
- Comparison: BCO is the only compared method without access to demonstrator actions, yet it can achieve comparable performance while requiring far fewer environment interactions.The comparison emphasizes learning from state trajectories rather than state-action demonstrations.
- Training Procedure: BCO and BC use 70% of available data for training and 30% for validation, stopping when validation error begins to increase.Other methods use all available data for training.
- Interaction Efficiency: At least 40 times more interactions are required by GAIL or FEM to reach the performance attained by BCO(0).BCO(0) performs all environment interactions before policy learning begins, so its performance is shown independently of post-demonstration interactions.
- Task Results: BCO performs as well as the expert in CartPole, worse than other methods in MountainCar, better than GAIL with fewer demonstrations in Reacher, and almost as well as GAIL in Ant.The reported comparisons concern final performance as the number of demonstration trajectories varies.
- Effect of α: Increasing α improves BCO performance because additional post-demonstration interactions allow better estimates of the demonstrator’s actions.Figure 6 reports mean returns and standard errors over 5000 trajectories; CartPole is omitted because results were perfect for every α.
6 Conclusions
The paper presents BCO as imitation learning without demonstrator actions or post-demonstration environment interaction. Its experiments report favorable performance relative to action-informed approaches while requiring fewer post-demonstration interactions and less delay.
- Conclusion: BCO performs imitation learning without access to demonstrator actions or post-demonstration environment interaction.This directly addresses both constraints emphasized in the paper’s conclusion.
- Conclusion: BCO policies perform favorably compared with existing imitation-learning approaches that require demonstrator action information.The conclusion frames this as an experimental comparison rather than a universal guarantee.
- Conclusion: Fewer post-demonstration interactions allow a reasonable imitation policy to be executed with less delay.The conclusion links reduced interaction requirements to execution delay.