Reinforcement Learning and Dopamine

The rl package provides core infrastructure for dopamine neuromodulation and reinforcement learning, including the Rescorla-Wagner learning algorithm (RW) and Temporal Differences (TD) learning, and a minimal ClampDaLayer that can be used to send an arbitrary DA signal.

• da.go defines a simple DALayer interface for getting and setting dopamine values, and a SendDA list of layer names that has convenience methods, and ability to send dopamine to any layer that implements the DALayer interface.

• The RW and TD DA layers use the CyclePost layer-level method to send the DA to other layers, at end of each cycle, after activation is updated. Thus, DA lags by 1 cycle, which typically should not be a problem.

• See the separate pvlv package for the full biologically-based pvlv model on top of this basic DA infrastructure.