Imitation from Observation With Bootstrapped Contrastive Learning

• URL: http://arxiv.org/abs/2302.06540v1
• Date: Mon, 13 Feb 2023 17:32:17 GMT
• Title: Imitation from Observation With Bootstrapped Contrastive Learning
• Authors: Medric Sonwa, Johanna Hansen, Eugene Belilovsky
• Abstract summary: Imitation from observation (IfO) is a learning paradigm that consists of training autonomous agents in a Markov Decision Process. We present BootIfOL, an IfO algorithm that aims to learn a reward function that takes an agent trajectory and compares it to an expert. We evaluate our approach on a variety of control tasks showing that we can train effective policies using a limited number of demonstrative trajectories.
• Score: 12.048166025000976
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Imitation from observation (IfO) is a learning paradigm that consists of training autonomous agents in a Markov Decision Process (MDP) by observing expert demonstrations without access to its actions. These demonstrations could be sequences of environment states or raw visual observations of the environment. Recent work in IfO has focused on this problem in the case of observations of low-dimensional environment states, however, access to these highly-specific observations is unlikely in practice. In this paper, we adopt a challenging, but more realistic problem formulation, learning control policies that operate on a learned latent space with access only to visual demonstrations of an expert completing a task. We present BootIfOL, an IfO algorithm that aims to learn a reward function that takes an agent trajectory and compares it to an expert, providing rewards based on similarity to agent behavior and implicit goal. We consider this reward function to be a distance metric between trajectories of agent behavior and learn it via contrastive learning. The contrastive learning objective aims to closely represent expert trajectories and to distance them from non-expert trajectories. The set of non-expert trajectories used in contrastive learning is made progressively more complex by bootstrapping from roll-outs of the agent learned through RL using the current reward function. We evaluate our approach on a variety of control tasks showing that we can train effective policies using a limited number of demonstrative trajectories, greatly improving on prior approaches that consider raw observations.

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