Search Results for author:
Found 15 papers, 5 papers with code
To the Max: Reinventing Reward in Reinforcement Learning
In reinforcement learning (RL), different rewards can define the same optimal policy but result in drastically different learning performance.
You Shall Pass: Dealing with the Zero-Gradient Problem in Predict and Optimize for Convex Optimization
The key challenge to train such models is the computation of the Jacobian of the solution of the optimization problem with respect to its parameters.
Diverse Projection Ensembles for Distributional Reinforcement Learning
In contrast to classical reinforcement learning, distributional reinforcement learning algorithms aim to learn the distribution of returns rather than their expected value.
The Role of Diverse Replay for Generalisation in Reinforcement Learning
We motivate mathematically and show empirically that generalisation to tasks that are "reachable'' during training is improved by increasing the diversity of transitions in the replay buffer.
Active Classification of Moving Targets with Learned Control Policies
In this paper, we consider the problem where a drone has to collect semantic information to classify multiple moving targets.
E-MCTS: Deep Exploration in Model-Based Reinforcement Learning by Planning with Epistemic Uncertainty
One of the most well-studied and highly performing planning approaches used in Model-Based Reinforcement Learning (MBRL) is Monte-Carlo Tree Search (MCTS).
Model-based Reinforcement Learning
reinforcement-learning
+1
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning
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.
Multi-agent Reinforcement Learning
reinforcement-learning
+3
Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning
Multi-agent settings in the real world often involve tasks with varying types and quantities of agents and non-agent entities; however, common patterns of behavior often emerge among these agents/entities.
FACMAC: Factored Multi-Agent Centralised Policy Gradients
We propose FACtored Multi-Agent Centralised policy gradients (FACMAC), a new method for cooperative multi-agent reinforcement learning in both discrete and continuous action spaces.
Optimistic Exploration even with a Pessimistic Initialisation
We show that this scheme is provably efficient in the tabular setting and extend it to the deep RL setting.
Deep Coordination Graphs
This paper introduces the deep coordination graph (DCG) for collaborative multi-agent reinforcement learning.
Exploration with Unreliable Intrinsic Reward in Multi-Agent Reinforcement Learning
This paper investigates the use of intrinsic reward to guide exploration in multi-agent reinforcement learning.
Multitask Soft Option Learning
We present Multitask Soft Option Learning(MSOL), a hierarchical multitask framework based on Planning as Inference.
Non-Deterministic Policy Improvement Stabilizes Approximated Reinforcement Learning
This paper investigates a type of instability that is linked to the greedy policy improvement in approximated reinforcement learning.
Regression with Linear Factored Functions
Many applications that use empirically estimated functions face a curse of dimensionality, because the integrals over most function classes must be approximated by sampling.
