Added readme and requirements

README.md

 # Bipedal Walker Evo
 
-Trying to solve the bipedal walker with an evolution algorithm
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+This project tries to solve OpenAI's bipedal walker with an evolutionary strategy.\
+After 1000 episodes, which is about 1h of learning, it will reach ~250 reward.\
+Best score until now: 292/300
+
+## How it works
+1. Generate a randomly weighted neural net
+2. Create a population of neural nets with mutated weights
+3. Let every net finish an episode and reward it accordingly
+4. The better the reward, the higher the chance to pass weights to next gen
+
+## Hyperparameters
+| Parameter         | Description                                                 | Interval  |
+|-------------------|-------------------------------------------------------------|-----------|
+| `HIDDEN_LAYER`    | Size of hidden layer.                                       | [1;∞[     |
+| `POP_SIZE`        | Size of population.                                         | [0;∞[     |
+| `MUTATION_FACTOR` | Percentage of weights that will be mutated for each mutant. | [0;1]     |
+| `LEARNING_RATE`   | This is the rate of learning.                               | [0;1]     |
+| `GENS`            | Number of generations.                                      | [0;1]     |
+| `MAX_STEPS`       | Number of steps that are played in one episode.             | [0; 1600] |
+
+
+## Installation
+We use Windows, Anaconda and Python 3.7 \
+` conda create -n evo_neuro python=3.7` \
+`conda activate evo_neuro`\
+`conda install swig`\
+`pip install -r requirements.txt`\
+
+
+
+## Sources
+Environment: https://github.com/openai/gym/wiki/BipedalWalker-v2 \
+OpenAI Website: https://gym.openai.com/envs/BipedalWalker-v2/ \
+More on evolution strategies: https://openai.com/blog/evolution-strategies/
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main.py

@@ -8,12 +8,12 @@ HIDDEN_LAYER = 12
 POP_SIZE = 50
 MUTATION_FACTOR = 0.1  # 0 <= x <= 1
 LEARNING_RATE = 0.03   # 0 <= x <= 1
-GENS = 1000
-MAX_STEPS = 300  # after 1600 steps the Environment gives us a done anyway.
+GENS = 3000
+MAX_STEPS = 1200  # after 1600 steps the Environment gives us a done anyway.
 
 
 VERSION = 1
-TEST_WALKER = True
+TEST_WALKER = False
 LOAD_BRAIN = False
 RENDER_BEST = False
 if TEST_WALKER:

population.py

 import numpy as np
-import random
-import logging
-import copy
 from walker import Walker
 import gym
 

walker.py

-import gym
 import numpy as np
 import pickle
 import copy