NoisyNetDQN

A TensorFlow implementation of "Noisy network for exploration".

The Q-value function is implemented with 2 convolution layers and 3 fully connected layers, and I use the atari game Breakout-v0 for the test.

If you are doing test on the CartPole or some other games which the state is 1 dimension, there network of Q-value function should only have dense layers.

As a comparison, you can see the implementation of DQN in DQN.py.

The feedback after an action contains 4 parts:

state, real-reward, game_over, lives_rest

There are 4 actions in the game Breakout-v0:

0: hold and do nothing
1: throw the ball
2: move right
3: move left

Note: The train-reward is equal to the real-reward in most of the time. But in the beginning of the new live, Its obvious the action should be throwing the ball. So the train-reward should be 1 in spite of the real-reward of throwing is zero. And the train-reward is -1 when you miss the ball.