Imitation Learning

265 papers with code • 0 benchmarks • 16 datasets

Imitation Learning is a framework for learning a behavior policy from demonstrations. Usually, demonstrations are presented in the form of state-action trajectories, with each pair indicating the action to take at the state being visited. In order to learn the behavior policy, the demonstrated actions are usually utilized in two ways. The first, known as Behavior Cloning (BC), treats the action as the target label for each state, and then learns a generalized mapping from states to actions in a supervised manner. Another way, known as Inverse Reinforcement Learning (IRL), views the demonstrated actions as a sequence of decisions, and aims at finding a reward/cost function under which the demonstrated decisions are optimal.

Source: Adversarial Imitation Learning from Incomplete Demonstrations

Greatest papers with code

Primal Wasserstein Imitation Learning

google-research/google-research ICLR 2021

Imitation Learning (IL) methods seek to match the behavior of an agent with that of an expert.

Continuous Control Fine-tuning +1

TRAIL: Near-Optimal Imitation Learning with Suboptimal Data

google-research/google-research 27 Oct 2021

In this work, we answer this question affirmatively and present training objectives that use offline datasets to learn a factored transition model whose structure enables the extraction of a latent action space.

Imitation Learning

Provable Representation Learning for Imitation with Contrastive Fourier Features

google-research/google-research NeurIPS 2021

In imitation learning, it is common to learn a behavior policy to match an unknown target policy via max-likelihood training on a collected set of target demonstrations.

Atari Games Contrastive Learning +2

An Imitation Learning Approach for Cache Replacement

google-research/google-research ICML 2020

While directly applying Belady's is infeasible since the future is unknown, we train a policy conditioned only on past accesses that accurately approximates Belady's even on diverse and complex access patterns, and call this approach Parrot.

Imitation Learning

Imitation Learning via Off-Policy Distribution Matching

google-research/google-research ICLR 2020

In this work, we show how the original distribution ratio estimation objective may be transformed in a principled manner to yield a completely off-policy objective.

Imitation Learning

Generative Adversarial Imitation Learning

hill-a/stable-baselines NeurIPS 2016

Consider learning a policy from example expert behavior, without interaction with the expert or access to reinforcement signal.

Imitation Learning

From Motor Control to Team Play in Simulated Humanoid Football

deepmind/dm_control 25 May 2021

In a sequence of stages, players first learn to control a fully articulated body to perform realistic, human-like movements such as running and turning; they then acquire mid-level football skills such as dribbling and shooting; finally, they develop awareness of others and play as a team, bridging the gap between low-level motor control at a timescale of milliseconds, and coordinated goal-directed behaviour as a team at the timescale of tens of seconds.

Decision Making Imitation Learning +2

The Arcade Learning Environment: An Evaluation Platform for General Agents

mgbellemare/Arcade-Learning-Environment 19 Jul 2012

We illustrate the promise of ALE by developing and benchmarking domain-independent agents designed using well-established AI techniques for both reinforcement learning and planning.

Atari Games Imitation Learning +1

Modeling the Long Term Future in Model-Based Reinforcement Learning

maximecb/gym-minigrid ICLR 2019

This paper focuses on building a model that reasons about the long-term future and demonstrates how to use this for efficient planning and exploration.

Imitation Learning Model-based Reinforcement Learning +1

Guiding Policies with Language via Meta-Learning

maximecb/gym-minigrid ICLR 2019

However, a single instruction may be insufficient to fully communicate our intent or, even if it is, may be insufficient for an autonomous agent to actually understand how to perform the desired task.

Imitation Learning Meta-Learning