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        "description": "# BARL - Bayesian Approximate Reinforcement Learning\nThis package should serve as a collection of tools to do RL in general and in particular bayesian RL.\n\n## The Main Features(Jul 2019):\n1. estimators\n2. agents\n3. environments\n4. simulations & visualisation\n\n## Installation:\n\n### PIP:\n```bash\npip3 install barl\n```\n\n### Github:\n```bash\ngit clone https://github.com/ai-nikolai/barl\ncd barl\npip3 install -e .\n```\n\n## Usage:\n\n### Testing\n```bash\ncd barl\npytest\n```\n\n### Experiments:\n```bash\ncd barl\ncd experiments\npython3 experiments_mab.py\n```\n\n### Scripts:\n```python\nimport barl\n\nenv = barl.environments.MultiArmedBandit(arms=4)\n\nagent1 = barl.agents.baselines.RandomActionsSampler(numActions=4)\n\ntotal, arlist, _ = barl.simulations.run_state_less_agent_and_env( environment=env, agent=agent1, N=100)\n\nbarl.utils.plotting.plot_reward_over_time_from_ar(arlist)\n```\n\n\n\n## Copyright (C) - Nikolai Rozanov 2019-Present\n\n\n",
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