Training intelligent game agents using deep reinforcement learning
summary
Claims concrete outcomes: runnable notebooks show how deep reinforcement learning (DRL) agents train with python, using TensorFlow layers, a policy-gradient notebook with an entropy bonus, a double DQN notebook, and an advantage actor‑critic sketch. Implementation details include replay buffers, decoupled target network updates and Polyak averaging, and comparison of REINFORCE versus reparameterization tricks. Experiments on gym‑style rollouts sometimes converge but show high variance, sensitivity to hyperparameters, and occasional suboptimal steady states, so results are mixed rather than turnkey.
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chapters (10)
- 0:00Deep reinforcement learning intro
- 7:30Reinforcement learning basics
- 10:02Markov decision processes
- 16:22Policy gradient methods
- 23:43Policy gradient demo
- 37:02Value-based methods
- 49:33Deep Q-learning challenges
- 58:49Actor-critic methods
- 1:05:41Advanced algorithms
- 1:11:58Limitations today
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