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Greatest papers with code

Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning

7 Aug 2017flowersteam/explauto

We present an algorithmic approach called Intrinsically Motivated Goal Exploration Processes (IMGEP) to enable similar properties of autonomous or self-supervised learning in machines.

CURRICULUM LEARNING MULTI-GOAL REINFORCEMENT LEARNING SELF-SUPERVISED LEARNING

Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning

ICML 2020 spitis/mrl

What goals should a multi-goal reinforcement learning agent pursue during training in long-horizon tasks?

MULTI-GOAL REINFORCEMENT LEARNING

Counterfactual Data Augmentation using Locally Factored Dynamics

NeurIPS 2020 spitis/mrl

Many dynamic processes, including common scenarios in robotic control and reinforcement learning (RL), involve a set of interacting subprocesses.

DATA AUGMENTATION GENERAL REINFORCEMENT LEARNING MULTI-GOAL REINFORCEMENT LEARNING

CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning

15 Oct 2018flowersteam/curious

In open-ended environments, autonomous learning agents must set their own goals and build their own curriculum through an intrinsically motivated exploration.

CURRICULUM LEARNING EFFICIENT EXPLORATION MULTI-GOAL REINFORCEMENT LEARNING

Maximum Entropy-Regularized Multi-Goal Reinforcement Learning

21 May 2019ruizhaogit/mep

This objective encourages the agent to maximize the expected return, as well as to achieve more diverse goals.

MULTI-GOAL REINFORCEMENT LEARNING OPENAI GYM

Learning to Reach Goals via Iterated Supervised Learning

ICLR 2021 dibyaghosh/gcsl

Current reinforcement learning (RL) algorithms can be brittle and difficult to use, especially when learning goal-reaching behaviors from sparse rewards.

MULTI-GOAL REINFORCEMENT LEARNING

ROLL: Visual Self-Supervised Reinforcement Learning with Object Reasoning

13 Nov 2020yufeiwang63/ROLL

Current image-based reinforcement learning (RL) algorithms typically operate on the whole image without performing object-level reasoning.

MULTI-GOAL REINFORCEMENT LEARNING

An Inductive Bias for Distances: Neural Nets that Respect the Triangle Inequality

ICLR 2020 spitis/deepnorms

When defining distances, the triangle inequality has proven to be a useful constraint, both theoretically--to prove convergence and optimality guarantees--and empirically--as an inductive bias.

METRIC LEARNING MULTI-GOAL REINFORCEMENT LEARNING

Learning Discrete State Abstractions With Deep Variational Inference

9 Mar 2020ondrejba/discrete_abstractions

In this work, we propose an information bottleneck method for learning approximate bisimulations, a type of state abstraction.

DECISION MAKING MULTI-GOAL REINFORCEMENT LEARNING VARIATIONAL INFERENCE