2 citations · 2 across the 4 of their papers we have counts for
6 papers
JaxMARL: Multi-Agent RL Environments and Algorithms in JAX
Alexander Rutherford, Benjamin Ellis, Matteo Gallici +18
Benchmarks are crucial in the development of machine learning algorithms, with available environments significantly influencing reinforcement learning (RL) research. Traditionally,…
Goal-Conditioned Agents that Learn Everything All at Once
Michael Matthews, Matthew Jackson, Michael Beukman +5
A goal-conditioned reinforcement learning agent exploring an environment will see a wealth of information throughout a trajectory, most of which is discarded when only performing o…
Abstraction for Offline Goal-Conditioned Reinforcement Learning
Clarisse Wibault, Alexander Goldie, Antonio Villares +2
Markov Decision Processes (MDPs) often exhibit significant redundancy due to symmetries and shared structure across state-goal pairs in real-world Goal-Conditioned Reinforcement Le…
Hierarchical Behaviour Spaces
Michael Tryfan Matthews, Anssi Kanervisto, Jakob Foerster +3
Recent work in hierarchical reinforcement learning has shown success in scaling to billions of timesteps when learning over a set of predefined option reward functions. We show tha…
Mirror Learning: A Unifying Framework of Policy Optimisation
Jakub Grudzien Kuba, Christian Schroeder de Witt, Jakob Foerster
Modern deep reinforcement learning (RL) algorithms are motivated by either the generalised policy iteration (GPI) or trust-region learning (TRL) frameworks. However, algorithms tha…
ReLU to the Rescue: Improve Your On-Policy Actor-Critic with Positive Advantages
Andrew Jesson, Chris Lu, Gunshi Gupta +4
This paper proposes a step toward approximate Bayesian inference in on-policy actor-critic deep reinforcement learning. It is implemented through three changes to the Asynchronous…