Multi-Goal Reinforcement Learning

16 papers with code • 0 benchmarks • 1 datasets

This task has no description! Would you like to contribute one?

Datasets


Most implemented papers

Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning

ymollard/APEX 7 Aug 2017

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

Maximum Entropy-Regularized Multi-Goal Reinforcement Learning

ruizhaogit/mep 21 May 2019

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

Learning to Reach Goals via Iterated Supervised Learning

dibyaghosh/gcsl ICLR 2021

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

An Open-Source Multi-Goal Reinforcement Learning Environment for Robotic Manipulation with Pybullet

IanYangChina/pybullet_multigoal_gym 12 May 2021

This work re-implements the OpenAI Gym multi-goal robotic manipulation environment, originally based on the commercial Mujoco engine, onto the open-source Pybullet engine.

CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning

flowersteam/curious 15 Oct 2018

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

Bias-Reduced Hindsight Experience Replay with Virtual Goal Prioritization

filipolszewski/prioritized-her 14 May 2019

We call this property the instructiveness of the virtual goal and define it by a heuristic measure, which expresses how well the agent will be able to generalize from that virtual goal to actual goals.

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

spitis/deepnorms ICLR 2020

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.

Learning Discrete State Abstractions With Deep Variational Inference

ondrejba/discrete_abstractions pproximateinference AABI Symposium 2021

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

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

spitis/mrl ICML 2020

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