This repo contains code that trains a reinforcement learning algorithm on the OpenAI Gym Atari environments (although you can alter the environment in the config file and it should all work with environments that are two dimensional in input fine; nothing depends on the environment except the assumption of a two dimensional observation space and a one dimensional control space). It is inspired by the implementations (not by me!):
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An anaconda distribution
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opencv
| Layer | Input | Filter size | Strides | Number of filters | Activation function | Output size |
|---|---|---|---|---|---|---|
| conv1 | 84 x 84 x 4 | 8 x 8 | 4 | 32 | ReLU | 20 x 20 x 32 |
| conv2 | 20 x 20 x 32 | 4 x 4 | 2 | 64 | ReLU | 9 x 9 x 64 |
| conv3 | 9 x 9 x 64 | 3 x 3 | 1 | 64 | ReLU | 7 x 7 x 64 |
| fc1 | flattened 7 x 7 x 64 | 512 | ReLU | 512 | ||
| fc2 | Number of actions | Linear | Number of actions |
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