Different reinforcement learning implementations for solving cartpole problem
Python 100.00%
cartpole's Introduction
script.py
the script to train the agent to get 200 rewards consistently in cart pole
import any definition of the agent from the following files to run
naive_dqn.py
deep Q-learning with only replay memory
unstable, typically needs a lot more than 1k episodes to train
tried to adjust epsilon according to discrepency between the target score of 200 and current reward, but the high variance of training rewards prevents meaningful comparison, no improvement could be noticed
double_dqn.py
DQN with both replay memory and a target network that implements fixed Q update(Double DQN paper)
relatively more stable, typically needs around 1k episodes to converge, but often would require a lot of episodes
did not observe significant improvements over the naive dqn
policy_net.py
Policy Gradient method implemented in tensorflow
It takes "forever" to converge with naive reward
Instead, we use a normalized advantage value, outperforming DQN significantly
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