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Imitation Learning

162 papers with code · Methodology

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

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Greatest papers with code

Primal Wasserstein Imitation Learning

ICLR 2021 google-research/google-research

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

CONTINUOUS CONTROL IMITATION LEARNING

An Imitation Learning Approach for Cache Replacement

ICML 2020 google-research/google-research

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

ICLR 2020 google-research/google-research

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

NeurIPS 2016 hill-a/stable-baselines

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

IMITATION LEARNING

The Arcade Learning Environment: An Evaluation Platform for General Agents

19 Jul 2012mgbellemare/Arcade-Learning-Environment

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 TRANSFER LEARNING

Neural Modular Control for Embodied Question Answering

26 Oct 2018facebookresearch/House3D

We use imitation learning to warm-start policies at each level of the hierarchy, dramatically increasing sample efficiency, followed by reinforcement learning.

EMBODIED QUESTION ANSWERING IMITATION LEARNING QUESTION ANSWERING

Modeling the Long Term Future in Model-Based Reinforcement Learning

ICLR 2019 maximecb/gym-minigrid

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

Guiding Policies with Language via Meta-Learning

ICLR 2019 maximecb/gym-minigrid

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

STARDATA: A StarCraft AI Research Dataset

7 Aug 2017TorchCraft/StarData

We provide full game state data along with the original replays that can be viewed in StarCraft.

IMITATION LEARNING STARCRAFT

BabyAI 1.1

24 Jul 2020mila-iqia/babyai

This increases reinforcement learning sample efficiency by up to 3 times and improves imitation learning performance on the hardest level from 77 % to 90. 4 %.

IMITATION LEARNING