50 citations · 163 across the 21 of their papers we have counts for
13 papers · 1 filter
Generalization in offline RL: The structure is more important than the amount of pessimism
Max Weltevrede, Matthijs T. J. Spaan, Wendelin Böhmer
While pessimism counteracts overestimation bias in offline reinforcement learning (RL), being overly conservative has been associated with hindering certain forms of generalization…
EfficientTDMPC: Improved MPC Objectives for Sample-Efficient Continuous Control
Thomas Evers, Cristian Meo, Wendelin Bohmer +2
We introduce EfficientTDMPC, a sample-efficient model-based reinforcement learning method for continuous control built on the TD-MPC family of algorithms. Central to this family is…
Particle Monte Carlo Tree Search
Yaniv Oren, Viliam Vadocz, Joery A. de Vries +3
Monte Carlo Tree Search (MCTS) is a widely used approach for policy improvement and action selection in Reinforcement Learning. Due to its sequential and deterministic nature, prin…
Explore-Go: Leveraging Exploration for Generalisation in Deep Reinforcement Learning
Max Weltevrede, Felix Kaubek, Matthijs T. J. Spaan +1
One of the remaining challenges in reinforcement learning is to develop agents that can generalise to novel scenarios they might encounter once deployed. This challenge is often fr…
Diverse Projection Ensembles for Distributional Reinforcement Learning
Moritz A. Zanger, Wendelin Böhmer, Matthijs T. J. Spaan
In contrast to classical reinforcement learning (RL), distributional RL algorithms aim to learn the distribution of returns rather than their expected value. Since the nature of th…
The Role of Diverse Replay for Generalisation in Reinforcement Learning
Max Weltevrede, Matthijs T. J. Spaan, Wendelin Böhmer
In reinforcement learning (RL), key components of many algorithms are the exploration strategy and replay buffer. These strategies regulate what environment data is collected and t…
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning
Tarun Gupta, Anuj Mahajan, Bei Peng +2
VDN and QMIX are two popular value-based algorithms for cooperative MARL that learn a centralized action value function as a monotonic mixing of per-agent utilities. While this ena…
Optimistic Exploration even with a Pessimistic Initialisation
Tabish Rashid, Bei Peng, Wendelin Böhmer +1
Optimistic initialisation is an effective strategy for efficient exploration in reinforcement learning (RL). In the tabular case, all provably efficient model-free algorithms rely…
Deep Coordination Graphs
Wendelin Böhmer, Vitaly Kurin, Shimon Whiteson
This paper introduces the deep coordination graph (DCG) for collaborative multi-agent reinforcement learning. DCG strikes a flexible trade-off between representational capacity and…
Deep Residual Reinforcement Learning
Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson
We revisit residual algorithms in both model-free and model-based reinforcement learning settings. We propose the bidirectional target network technique to stabilize residual algor…
Multitask Soft Option Learning
Maximilian Igl, Andrew Gambardella, Jinke He +4
We present Multitask Soft Option Learning(MSOL), a hierarchical multitask framework based on Planning as Inference. MSOL extends the concept of options, using separate variational…
Generalized Off-Policy Actor-Critic
Shangtong Zhang, Wendelin Boehmer, Shimon Whiteson
We propose a new objective, the counterfactual objective, unifying existing objectives for off-policy policy gradient algorithms in the continuing reinforcement learning (RL) setti…
Regression with Linear Factored Functions
Wendelin Böhmer, Klaus Obermayer
Many applications that use empirically estimated functions face a curse of dimensionality, because the integrals over most function classes must be approximated by sampling. This p…