Search Results for author:
Found 19 papers, 11 papers with code
Explicitly Disentangled Representations in Object-Centric Learning
Extracting structured representations from raw visual data is an important and long-standing challenge in machine learning.
EduGym: An Environment and Notebook Suite for Reinforcement Learning Education
To solve this issue we introduce EduGym, a set of educational reinforcement learning environments and associated interactive notebooks tailored for education.
What model does MuZero learn?
Model-based reinforcement learning has drawn considerable interest in recent years, given its promise to improve sample efficiency.
First Go, then Post-Explore: the Benefits of Post-Exploration in Intrinsic Motivation
In this paper, we present a clear ablation study of post-exploration in a general intrinsically motivated goal exploration process (IMGEP) framework, that the Go-Explore paper did not show.
Continuous Episodic Control
Therefore, this paper introduces Continuous Episodic Control (CEC), a novel non-parametric episodic memory algorithm for sequential decision making in problems with a continuous action space.
When to Go, and When to Explore: The Benefit of Post-Exploration in Intrinsic Motivation
Go-Explore achieved breakthrough performance on challenging reinforcement learning (RL) tasks with sparse rewards.
On Credit Assignment in Hierarchical Reinforcement Learning
To improve our fundamental understanding of HRL, we investigate hierarchical credit assignment from the perspective of conventional multistep reinforcement learning.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Visualizing MuZero Models
In contrast to standard forward dynamics models that predict a full next state, value equivalent models are trained to predict a future value, thereby emphasizing value relevant information in the representations.
Model-based Reinforcement Learning: A Survey
Two key approaches to this problem are reinforcement learning (RL) and planning.
A Unifying Framework for Reinforcement Learning and Planning
Therefore, this paper presents a unifying algorithmic framework for reinforcement learning and planning (FRAP), which identifies underlying dimensions on which MDP planning and learning algorithms have to decide.
The Second Type of Uncertainty in Monte Carlo Tree Search
Monte Carlo Tree Search (MCTS) efficiently balances exploration and exploitation in tree search based on count-derived uncertainty.
Think Too Fast Nor Too Slow: The Computational Trade-off Between Planning And Reinforcement Learning
Planning and reinforcement learning are two key approaches to sequential decision making.
The Potential of the Return Distribution for Exploration in RL
This paper studies the potential of the return distribution for exploration in deterministic reinforcement learning (RL) environments.
A0C: Alpha Zero in Continuous Action Space
A core novelty of Alpha Zero is the interleaving of tree search and deep learning, which has proven very successful in board games like Chess, Shogi and Go.
Monte Carlo Tree Search for Asymmetric Trees
Asymmetric termination of search trees introduces a type of uncertainty for which the standard upper confidence bound (UCB) formula does not account.
Efficient exploration with Double Uncertain Value Networks
This paper studies directed exploration for reinforcement learning agents by tracking uncertainty about the value of each available action.
Emotion in Reinforcement Learning Agents and Robots: A Survey
This article provides the first survey of computational models of emotion in reinforcement learning (RL) agents.
Learning Multimodal Transition Dynamics for Model-Based Reinforcement Learning
In this paper we study how to learn stochastic, multimodal transition dynamics in reinforcement learning (RL) tasks.
Model-based Reinforcement Learning
reinforcement-learning
+2
