Investigating Reinforcement Learning Agents for Continuous State Space Environments

2017-08-08Unverified0· sign in to hype

David Von Dollen

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Abstract

Given an environment with continuous state spaces and discrete actions, we investigate using a Double Deep Q-learning Reinforcement Agent to find optimal policies using the LunarLander-v2 OpenAI gym environment.

Tasks

OpenAI Gym Q-Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Investigating Reinforcement Learning Agents for Continuous State Space Environments

Abstract

Tasks

Reproductions