424 citations · 860 across the 53 of their papers we have counts for
8 papers · 1 filter
Contextual Pre-planning on Reward Machine Abstractions for Enhanced Transfer in Deep Reinforcement Learning
Guy Azran, Mohamad H. Danesh, Stefano V. Albrecht +1
Recent studies show that deep reinforcement learning (DRL) agents tend to overfit to the task on which they were trained and fail to adapt to minor environment changes. To expedite…
SMAClite: A Lightweight Environment for Multi-Agent Reinforcement Learning
Adam Michalski, Filippos Christianos, Stefano V. Albrecht
There is a lack of standard benchmarks for Multi-Agent Reinforcement Learning (MARL) algorithms. The Starcraft Multi-Agent Challenge (SMAC) has been widely used in MARL research, b…
Conditional Mutual Information for Disentangled Representations in Reinforcement Learning
Mhairi Dunion, Trevor McInroe, Kevin Sebastian Luck +2
Reinforcement Learning (RL) environments can produce training data with spurious correlations between features due to the amount of training data or its limited feature coverage. T…
Using Offline Data to Speed Up Reinforcement Learning in Procedurally Generated Environments
Alain Andres, Lukas Schäfer, Stefano V. Albrecht +1
One of the key challenges of Reinforcement Learning (RL) is the ability of agents to generalise their learned policy to unseen settings. Moreover, training RL agents requires large…
Revisiting the Gumbel-Softmax in MADDPG
Callum Rhys Tilbury, Filippos Christianos, Stefano V. Albrecht
MADDPG is an algorithm in multi-agent reinforcement learning (MARL) that extends the popular single-agent method, DDPG, to multi-agent scenarios. Importantly, DDPG is an algorithm…
Causal Explanations for Sequential Decision-Making in Multi-Agent Systems
Balint Gyevnar, Cheng Wang, Christopher G. Lucas +2
We present CEMA: Causal Explanations in Multi-Agent systems; a framework for creating causal natural language explanations of an agent's decisions in dynamic sequential multi-agent…
Learning Complex Teamwork Tasks Using a Given Sub-task Decomposition
Elliot Fosong, Arrasy Rahman, Ignacio Carlucho +1
Training a team to complete a complex task via multi-agent reinforcement learning can be difficult due to challenges such as policy search in a large joint policy space, and non-st…
Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning
Lukas Schäfer, Oliver Slumbers, Stephen McAleer +3
Multi-agent reinforcement learning (MARL) requires agents to explore within a vast joint action space to find joint actions that lead to coordination. Existing value-based MARL alg…