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- W1507129658 abstract "Learning automata (LA) are policy iteration reinforcement learners that exhibit nice convergence properties in discrete action settings. Recently, a novel formulation for continuous action reinforcement learning automata was proposed (CARLA), featuring an interesting exploration strategy, which is well suited for learning from sparse reward signals. In this paper, we propose an improvement of the algorithm, and evaluate its impact on performance comparing with the original version, as well as with another continuous LA. The experimental evaluation is carried out both in simulation and on a real control setup, showing a clear advantage of our method, which also performs well in distributed control settings." @default.
- W1507129658 created "2016-06-24" @default.
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- W1507129658 date "2011-09-01" @default.
- W1507129658 modified "2023-09-26" @default.
- W1507129658 title "Improving the performance of Continuous Action Reinforcement Learning Automata" @default.
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