Deep Reinforcement Learning Experiments and Visualizations

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Discover the latest advancements in deep reinforcement learning through experiments and visualizations. Explore research on human-level control and Q-learning in computer vision. Dive into videos showcasing learning rates in various games like Breakout, Pong, and Seaquest. Access feature visualizations and graphs for Breakout and Space Invaders. Credits to Prof. Fred G. Martin and resources from Mnih et al.'s work.

  • Reinforcement Learning
  • Experiments
  • Visualizations
  • Deep Learning
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  1. Mnih, Volodymyr, et al. "Human-level control through deep reinforcement learning."Nature 518.7540(2015):529-533. Experimentsby:AshishBudhiraja Course:Advanced ComputerVision Instructor: Jia-BinHuang

  2. Qlearning

  3. Videos at different Learning Rate (Breakout) 77 epochs 200 epochs breakout 77 breakout 200

  4. Videos at different Learning Rate (Pong) 141 epochs 200 epochs pong 141 pong 200

  5. Videos at different Learning Rate (Seaquest) 178 epochs 200 epochs seaquest 178 seaquest 200

  6. Feature Visualization file:///home/ashishkb/RL_ACV/neon/simple_dqn/results/breakout.html

  7. Breakout Graphs

  8. Space Invaders Graphs

  9. Credits: 1)Prof. FRED G. MARTIN http://www.cs.uml.edu/ecg/index.php/AIfall16/PS3b 2)Mnih, Volodymyr, et al. "Human-level control through deep reinforcement learning." Nature 518.7540 (2015): 529-533. 3)https://github.com/devsisters/DQN-tensorflow 4)https://github.com/tambetm/simple_dqn

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