  Reinforcement learning (RL) algorithms allow artificial agents to improve
their selection of actions to increase rewarding experiences in their
environments. Temporal Difference (TD) Learning -- a model-free RL method -- is
a leading account of the midbrain dopamine system and the basal ganglia in
reinforcement learning. These algorithms typically learn a mapping from the
agent's current sensed state to a selected action (known as a policy function)
via learning a value function (expected future rewards). TD Learning methods
have been very successful on a broad range of control tasks, but learning can
become intractably slow as the state space of the environment grows. This has
motivated methods that learn internal representations of the agent's state,
effectively reducing the size of the state space and restructuring state
representations in order to support generalization. However, TD Learning
coupled with an artificial neural network, as a function approximator, has been
shown to fail to learn some fairly simple control tasks, challenging this
explanation of reward-based learning. We hypothesize that such failures do not
arise in the brain because of the ubiquitous presence of lateral inhibition in
the cortex, producing sparse distributed internal representations that support
the learning of expected future reward. The sparse conjunctive representations
can avoid catastrophic interference while still supporting generalization. We
provide support for this conjecture through computational simulations,
demonstrating the benefits of learned sparse representations for three
problematic classic control tasks: Puddle-world, Mountain-car, and Acrobot.
