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...and it's possible to train learning agents to sense and interact with a world described by high dimensional continuous vector spaces, for instance using conv
by ogrisel 11y ago
...and it's possible to train learning agents to sense and interact with a world described by high dimensional continuous vector spaces, for instance using conv nets (for sensing audio / video signals) and actor-critic to learn an continuous policy:
http://arxiv.org/abs/1509.02971 http://arxiv.org/abs/1509.02971
The fact that the (reinforcement) learning problem is hard or not is not directly related to whether the observation and action spaces are discrete or continuous.
- ogrisel 11y agosee also by the same team: http://arxiv.org/abs/1602.01783 http://arxiv.org/abs/1602.01783
- argonaut 11y agoThere is a near infinite number of such spaces.
- ogrisel 11y agoI don't understand why would the "number of spaces" matters. What matters is can you design a learning algorithm that performs well in interesting spaces such as: - discrete spaces such as atari games and go, - continuous spaces such as driving a car, controlling a robot or bid on a ad exchange.
- argonaut 11y agoA really really large number of distinct decisions that need to be made. A car only needs to control a small set of actions (wheels, engine, a couple others I'm missing). A game player only needs to choose from a small set of actions (e.g. place piece at position X, move left/right/up/down/jump).