cleanup, added assets, readme

Author: gsurma

.gitignore

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 # mypy
 .mypy_cache/
+
+./logs/

.idea/workspace.xml

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+<h3 align="center">
+  <img src="assets/atari_icon_web.png" width="300">
+</h3>
+
+# Atari
+
+Research Playground built on top of [OpenAI's Atari Gym](https://gym.openai.com/envs/#atari), prepared for implementing various Reinforcement Learning algorithms.
+
+It can emulate any of the following games:
+
+> ['Asterix', 'Asteroids',
+>                         'MsPacman', 'Kaboom', 'BankHeist', 'Kangaroo',
+>                         'Skiing', 'FishingDerby', 'Krull', 'Berzerk',
+>                         'Tutankham', 'Zaxxon', 'Venture', 'Riverraid',
+>                         'Centipede', 'Adventure', 'BeamRider', 'CrazyClimber',
+>                         'TimePilot', 'Carnival', 'Tennis', 'Seaquest',
+>                         'Bowling', 'SpaceInvaders', 'Freeway', 'YarsRevenge',
+>                         'RoadRunner', 'JourneyEscape', 'WizardOfWor',
+>                         'Gopher', 'Breakout', 'StarGunner', 'Atlantis',
+>                         'DoubleDunk', 'Hero', 'BattleZone', 'Solaris',
+>                         'UpNDown', 'Frostbite', 'KungFuMaster', 'Pooyan',
+>                         'Pitfall', 'MontezumaRevenge', 'PrivateEye',
+>                         'AirRaid', 'Amidar', 'Robotank', 'DemonAttack',
+>                         'Defender', 'NameThisGame', 'Phoenix', 'Gravitar',
+>                         'ElevatorAction', 'Pong', 'VideoPinball', 'IceHockey',
+>                         'Boxing', 'Assault', 'Alien', 'Qbert', 'Enduro',
+>                         'ChopperCommand', 'Jamesbond']
+
+## Purpose
+Ultimate goal of this project is to implement and compare various RL approaches with atari games as a common denominator.
+
+## Usage
+
+1. Clone the repo.
+2. Go to the project's root folder.
+3. Install required packages`pip install -r requirements.txt`.
+4. Launch atari. I recommend starting with help command to see all available modes `python atari.py --help`.
+
+
+## Modes
+
+All below modes were benchmarked using following
+
+
+10M - 85h on Tesla K80
+5M ~40h on Tesla K80
+
+Breakout (human: 28.3)
+- [ ] DDQN
+- [ ] GE
+
+SpaceInvaders (human: 372.5)
+- [ ] DDQN
+- [ ] GE
+
+
+
+
+GAMMA = 0.99
+MEMORY_SIZE = 900000
+BATCH_SIZE = 32
+TRAINING_FREQUENCY = 4
+TARGET_NETWORK_UPDATE_FREQUENCY = TRAINING_FREQUENCY*10000
+MODEL_PERSISTENCE_UPDATE_FREQUENCY = 10000
+REPLAY_START_SIZE = 50000
+
+EXPLORATION_MAX = 1.0
+EXPLORATION_MIN = 0.1
+EXPLORATION_TEST = 0.01
+EXPLORATION_STEPS = 850000
+EXPLORATION_DECAY = (EXPLORATION_MAX-EXPLORATION_MIN)/EXPLORATION_STEPS
+
+
+    def __init__(self, input_shape, action_space):
+        self.model = Sequential()
+        self.model.add(Conv2D(32,
+                              8,
+                              strides=(4, 4),
+                              padding="valid",
+                              activation="relu",
+                              input_shape=input_shape,
+                              data_format="channels_first"))
+        self.model.add(Conv2D(64,
+                              4,
+                              strides=(2, 2),
+                              padding="valid",
+                              activation="relu",
+                              input_shape=input_shape,
+                              data_format="channels_first"))
+        self.model.add(Conv2D(64,
+                              3,
+                              strides=(1, 1),
+                              padding="valid",
+                              activation="relu",
+                              input_shape=input_shape,
+                              data_format="channels_first"))
+        self.model.add(Flatten())
+        self.model.add(Dense(512, activation="relu"))
+        self.model.add(Dense(action_space))
+        self.model.compile(loss="mean_squared_error",
+                           optimizer=RMSprop(lr=0.00025,
+                                             rho=0.95,
+                                             epsilon=0.01),
+                           metrics=["accuracy"])
+        self.model.summary()