Learning from Reinforcements
We look at the task of learning from reinforcements. One-step connectionist (neural
Q-Learning was implemented to act as a controller for the agent class GeislerPlayer. An
instance of this player can be viewed in the AgentWorld program and is labeled as
net enhance 2. The backpropogation algorithm is used as the policy function.
Summary of Results
To run the agent world, download the Windows 9x/NTAgentWorld.exe
executable and the jar file, or download the following class files and run using jview or your
favorite java application executor. For details
behind the algorithm or suggestions email email@example.com. Please stay tuned, an applet
version comming soon!