Reinforcement Learning with Parameterized Actions

Understand

We introduce a model-free algorithm for learning in Markov decision processes with parameterized actions-discrete actions with continuous parameters.

• At each step the agent must select both which action to use and which parameters to use with that action.
• We introduce the Q-PAMDP algorithm for learning in these domains, show that it converges to a local optimum, and compare it to direct policy search in the goal-scoring and Platform domains.