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This stack contains code that implements trajectory-based reinforcement learning. Policies are contained in the policy_library package, currently contains DMPPolicy (Dynamic Movement Primitives), and Covariant Trajectory Policy (discretized trajectories). The PI^2 (Policy Improvement with Path Integrals, Theodorou et al, 2010) algorithm is implemented in the policy_improvement package.
A "Task" is a motion that the robot can perform, along with an associated cost function. Once a task is implemented, the system will perform the task repeatedly and optimize the cost function. New "Task"s can be implemented by deriving from the "Task" abstract class in task_manager. Examples tasks are contained in "pr2_tasks".
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Wiki: policy_learning (last edited 2012-01-19 14:58:31 by KarlGlatz)
