Academic Work - Reinforcement Learning

• DQN Agents

  The document demonstrates reinforcement learning through the use of deep Q-network (DQN) agents by displaying how the agent an be used for both the Mountain Cart and Lunar Lander environments. The agent receives rewards and penalties for its actions as a way to teach the agent to make the most desirable decisions.

• Agentic RL

  This document demonstrates establishing a breakout environment and training a proximal policy optimization agent to how to behave within the environment.