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    "info": {
        "author": "Jo\u00e3o Ribeiro",
        "author_email": "jmribeiro77209@gmail.com",
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        "classifiers": [
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        "description": "# YAAF: Yet Another Agents Framework\n\n_A minimalistic reinforcement learning framework._\n\n## Installation \n\n      $ pip install yaaf                               # Install YAAF\n      $ pip install gym[atari]                         # Install OpenAI Gym's Atari environments\n      $ git clone https://github.com/jmribeiro/yaaf    # Clone the repo for the examples and tutorials\n      $ cd yaaf/examples                               \n      $ python 0_space_invaders_random_agent.py        # Run example\n\n- **Rapid Prototyping**: Setting up an agent on an environment can easily be done with a few lines of code:\n\n      # Setup the Environment\n      environment = OpenAIGymEnvironment(name=\"SpaceInvaders-v0\", render=True)\n\n      # Setup the Agent\n      agent = RandomAgent(environment.action_space)\n\n      # Run the agent on the environment for 5 episodes\n      runner = EpisodeRunners(agent, environment, episodes=5)\n      runner.run()\n      environment.close()\n\n- **Simplicity**\n\n    **States**: Tensors/Numpy arrays with a given shape;\n\n    **Actions**: Integers if a discrete action space, Floats if a continuous action space;\n\n    **Timesteps**: Named tuples in shape of (state, action, reward, next_, is_terminal, info)\n\n    **Agents**: Objects capable of execution actions when given a state. If trainable, capable of learning when given a timestep \n\n    **Environments**: Objects which evolve over time, where agents can execute actions;\n\n    **Runners**: Objects that interact an actor (agent or policy) on an environment, notifying a list of observers at every timestep;\n\n    **Metrics**: Objects that evaluate an agent's or policy's performance on a given run. Passed as observables to the Runners.\n\n    **Presenters** (TODO): Objects that display an agent's performance on a given run. Used to make plots and compare agents.\n\n- **Result Reproducibility**:\n\n    After creating and evaluating an agent on a given environment, it is possible to persistently save the agent to disk, allowing programmers to load up pre-trained agents and compare them in different scenarios.\n\n    **Implemented Agents**:\n\n    - DQN (https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf)\n    - QNetwork (http://ml.informatik.uni-freiburg.de/former/_media/publications/rieecml05.pdf)\n    - QLearning (https://link.springer.com/content/pdf/10.1007%2FBF00992698.pdf)\n    - SARSA\n\n    **Planned Agents**:\n\n    - [TODO] DDQN (https://arxiv.org/abs/1509.06461)\n    - [TODO] A3C (https://arxiv.org/pdf/1602.01783.pdf)\n    - [TODO] GA3C (https://arxiv.org/pdf/1611.06256.pdf)\n    - [TODO] DDPG (https://arxiv.org/pdf/1509.02971.pdf)\n    - [TODO] PPO (https://arxiv.org/abs/1707.06347)\n\n\n\n",
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        "license": "Apache 2.0",
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        "name": "yaaf",
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