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Found 11 papers, 3 papers with code
Symmetric Q-learning: Reducing Skewness of Bellman Error in Online Reinforcement Learning
In deep reinforcement learning, estimating the value function to evaluate the quality of states and actions is essential.
Robustifying a Policy in Multi-Agent RL with Diverse Cooperative Behaviors and Adversarial Style Sampling for Assistive Tasks
We demonstrate that policies trained with a popular deep RL method are vulnerable to changes in policies of other agents and that the proposed framework improves the robustness against such changes.
Motion Planning by Learning the Solution Manifold in Trajectory Optimization
The experimental results indicate that the trained model represents an infinite set of homotopic solutions for motion planning problems.
Discovering Diverse Solutions in Deep Reinforcement Learning by Maximizing State-Action-Based Mutual Information
In our method, a policy conditioned on a continuous or discrete latent variable is trained by directly maximizing the variational lower bound of the mutual information, instead of using the mutual information as unsupervised rewards as in previous studies.
Learning the Solution Manifold in Optimization and Its Application in Motion Planning
The experimental results indicate that the solution manifold can be learned with the proposed algorithm, and the trained model represents an infinite set of homotopic solutions for motion-planning problems.
Meta-Model-Based Meta-Policy Optimization
Model-based meta-reinforcement learning (RL) methods have recently been shown to be a promising approach to improving the sample efficiency of RL in multi-task settings.
Goal-Conditioned Variational Autoencoder Trajectory Primitives with Continuous and Discrete Latent Codes
The learned decoder can be used as a motion planner in which the user can specify the goal position and the trajectory types by setting the latent variables.
Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized Critics
Finally, we investigate the application of multi-agent methods to high-dimensional robotic tasks and show that our approach can be used to learn decentralized policies in this domain.
Multi-agent Reinforcement Learning
Reinforcement Learning (RL)
Hierarchical Reinforcement Learning via Advantage-Weighted Information Maximization
However, identifying the hierarchical policy structure that enhances the performance of RL is not a trivial task.
An Algorithmic Perspective on Imitation Learning
This process of learning from demonstrations, and the study of algorithms to do so, is called imitation learning.
Hierarchical Policy Search via Return-Weighted Density Estimation
Learning an optimal policy from a multi-modal reward function is a challenging problem in reinforcement learning (RL).
