Reinforcement Learning with Parameterized Actions

Code

• github.com/cycraig/MP-DQN
  pytorch★ 0
• github.com/cycraig/gym-goal
  none★ 0
• github.com/cycraig/gym-platform
  none★ 0

Abstract

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.

Tasks

Reproductions