Multi-Goal Reinforcement Learning
17 papers with code • 0 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Multi-Goal Reinforcement Learning
UniMiB SHAR
bipedal-skills
Latest papers
RoMo-HER: Robust Model-based Hindsight Experience Replay
In our paper, we design a robust framework called Robust Model-based Hindsight Experience Replay (RoMo-HER) which can effectively utilize the dynamical model in robot manipulation environments to enhance the sample efficiency.
Bilinear value networks
The dominant framework for off-policy multi-goal reinforcement learning involves estimating goal conditioned Q-value function.
Grounding Hindsight Instructions in Multi-Goal Reinforcement Learning for Robotics
We show that hindsight instructions improve the learning performance, as expected.
Multi-Goal Reinforcement Learning environments for simulated Franka Emika Panda robot
This technical report presents panda-gym, a set Reinforcement Learning (RL) environments for the Franka Emika Panda robot integrated with OpenAI Gym.
Adversarial Intrinsic Motivation for Reinforcement Learning
iDurugkar/adversarial-intrinsic-motivation
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NeurIPS 2021
In this paper, we investigate whether one such objective, the Wasserstein-1 distance between a policy's state visitation distribution and a target distribution, can be utilized effectively for reinforcement learning (RL) tasks.
An Open-Source Multi-Goal Reinforcement Learning Environment for Robotic Manipulation with Pybullet
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.
ROLL: Visual Self-Supervised Reinforcement Learning with Object Reasoning
yufeiwang63/ROLL
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Current image-based reinforcement learning (RL) algorithms typically operate on the whole image without performing object-level reasoning.
Maximum Entropy Gain Exploration for Long Horizon Multi-goal Reinforcement Learning
spitis/mrl
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ICML 2020
What goals should a multi-goal reinforcement learning agent pursue during training in long-horizon tasks?
Counterfactual Data Augmentation using Locally Factored Dynamics
Many dynamic processes, including common scenarios in robotic control and reinforcement learning (RL), involve a set of interacting subprocesses.
Learning Discrete State Abstractions With Deep Variational Inference
ondrejba/discrete_abstractions
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pproximateinference AABI Symposium 2021
In this work, we propose an information bottleneck method for learning approximate bisimulations, a type of state abstraction.
