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Fully Distributed Actor-Critic Architecture for Multitask Deep Reinforcement Learning
We propose a fully distributed actor-critic architecture, named Diff-DAC, with application to multitask reinforcement learning (MRL).
Compatible features for Monotonic Policy Improvement
In this work we establish conditions under which the parametric approximation of the critic does not introduce bias to the updates of surrogate objective.
Adaptive Sensor Placement for Continuous Spaces
We consider the problem of adaptively placing sensors along an interval to detect stochastically-generated events.
Diff-DAC: Distributed Actor-Critic for Average Multitask Deep Reinforcement Learning
We propose a fully distributed actor-critic algorithm approximated by deep neural networks, named \textit{Diff-DAC}, with application to single-task and to average multitask reinforcement learning (MRL).
A Survey of Learning in Multiagent Environments: Dealing with Non-Stationarity
The key challenge in multiagent learning is learning a best response to the behaviour of other agents, which may be non-stationary: if the other agents adapt their strategy as well, the learning target moves.
