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# Imitation Learning Edit

109 papers with code · Methodology

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# Active Imitation Learning with Noisy Guidance

26 May 2020xkianteb/leaqi

Imitation learning algorithms provide state-of-the-art results on many structured prediction tasks by learning near-optimal search policies.

3
26 May 2020

# Automatic Discovery of Interpretable Planning Strategies

Our algorithm combines recent advances in imitation learning and program induction with a new clustering method for identifying a large subset of demonstrations that can be accurately described by a simple, high-performing decision rule.

0
24 May 2020

# BabyWalk: Going Farther in Vision-and-Language Navigation by Taking Baby Steps

10 May 2020Sha-Lab/babywalk

To this end, we propose BabyWalk, a new VLN agent that is learned to navigate by decomposing long instructions into shorter ones (BabySteps) and completing them sequentially.

4
10 May 2020

# Imitation Learning for Fashion Style Based on Hierarchical Multimodal Representation

13 Apr 2020AemikaChow/DATASOURCE

In this work, we propose an adversarial inverse reinforcement learning formulation to recover reward functions based on hierarchical multimodal representation (HM-AIRL) during the imitation process.

5
13 Apr 2020

# Sparse Graphical Memory for Robust Planning

13 Mar 2020scottemmons/sgm

We wish to combine the strengths of deep learning and classical planning to solve long-horizon tasks from raw sensory input.

10
13 Mar 2020

# State-only Imitation with Transition Dynamics Mismatch

Imitation Learning (IL) is a popular paradigm for training agents to achieve complicated goals by leveraging expert behavior, rather than dealing with the hardships of designing a correct reward function.

4
27 Feb 2020

# Estimating Q(s,s') with Deep Deterministic Dynamics Gradients

21 Feb 2020uber-research/D3G

In this paper, we introduce a novel form of value function, $Q(s, s')$, that expresses the utility of transitioning from a state $s$ to a neighboring state $s'$ and then acting optimally thereafter.

13
21 Feb 2020

# Discriminator Soft Actor Critic without Extrinsic Rewards

19 Jan 2020dnishio/DSAC

The methods based on reinforcement learning, such as inverse reinforcement learning and generative adversarial imitation learning (GAIL), can learn from only a few expert data.

3
19 Jan 2020

# Multi-Agent Interactions Modeling with Correlated Policies

In this paper, we cast the multi-agent interactions modeling problem into a multi-agent imitation learning framework with explicit modeling of correlated policies by approximating opponents' policies, which can recover agents' policies that can regenerate similar interactions.

6
04 Jan 2020

# State-only Imitation with Transition Dynamics Mismatch

Imitation Learning (IL) is a popular paradigm for training agents to achieve complicated goals by leveraging expert behavior, rather than dealing with the hardships of designing a correct reward function.

4
01 Jan 2020