AIAug 8, 2017
Investigating Reinforcement Learning Agents for Continuous State Space Environments
arXiv:1708.02378v31 citations
Originality Synthesis-oriented
AI Analysis
This addresses the challenge of reinforcement learning in continuous spaces for researchers, but it is incremental as it applies an existing method to a standard environment.
The paper tackled the problem of finding optimal policies in continuous state space environments with discrete actions, achieving results using a Double Deep Q-learning agent on the LunarLander-v2 benchmark.
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.