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2008

Temporal Difference Based Actor Critic Learning - Convergence and Neural Implementation

11 years 1 months ago
Temporal Difference Based Actor Critic Learning - Convergence and Neural Implementation
Actor-critic algorithms for reinforcement learning are achieving renewed popularity due to their good convergence properties in situations where other approaches often fail (e.g., when function approximation is involved). Interestingly, there is growing evidence that actor-critic approaches based on phasic dopamine signals play a key role in biological learning through cortical and basal ganglia loops. We derive a temporal difference based actor critic learning algorithm, for which convergence can be proved without assuming widely separated time scales for the actor and the critic. The approach is demonstrated by applying it to networks of spiking neurons. The established relation between phasic dopamine and the temporal difference signal lends support to the biological relevance of such algorithms.
Dotan Di Castro, Dmitry Volkinshtein, Ron Meir
Added 29 Oct 2010
Updated 29 Oct 2010
Type Conference
Year 2008
Where NIPS
Authors Dotan Di Castro, Dmitry Volkinshtein, Ron Meir
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