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All about Deep Reinforcement Learning

I work in TensorFlow2 Framework

Environments

  • Snake_v1
    • Observation space: (RGB array or Extracted features)
    • Action space: Discrete(3), [Go Straight / Turn Left / Turn Right]
    • Reward scheme: (Dense or Sparse)

PPO agent after 50000 episodes

  • Snake_v2
    • Observation space: (RGB array)
    • Action space: Box(2), [Speed(0.0 ~ 1.0), Angle(-1.0 ~ 1.0)]
    • Reward scheme: (Dense or Sparse)

RL Agents

  • Randomly
  • TODOs
    • PPO (Proximal Policy Optimization)
    • DDPG (Deep Deterministic Policy Gradient)
    • A3C (Asynchronous Advantage Actor Critic)
    • DQN (Deep Q-Network)

IL Agents

  • TODOs
    • BC (Behavior Cloning)
    • GAIL (Generative Adversarial Imitation Learning)
    • VAIL (Variational GAIL)
    • DI-GAIL (Directed-Info GAIL)