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* Currently this is specific to the case of discretizing a continuous action space. Can easily be adapted to other case by feeding the kNN otherwise, and removing the usage of a discretizing output action filter
49 lines
1.2 KiB
ReStructuredText
49 lines
1.2 KiB
ReStructuredText
Agents
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======
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Coach supports many state-of-the-art reinforcement learning algorithms, which are separated into three main classes -
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value optimization, policy optimization and imitation learning.
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A detailed description of those algorithms can be found by navigating to each of the algorithm pages.
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.. image:: /_static/img/algorithms.png
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:width: 600px
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:align: center
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.. toctree::
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:maxdepth: 1
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:caption: Agents
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policy_optimization/ac
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policy_optimization/acer
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imitation/bc
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value_optimization/bs_dqn
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value_optimization/categorical_dqn
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imitation/cil
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policy_optimization/cppo
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policy_optimization/ddpg
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other/dfp
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value_optimization/double_dqn
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value_optimization/dqn
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value_optimization/dueling_dqn
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value_optimization/mmc
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value_optimization/n_step
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value_optimization/naf
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value_optimization/nec
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value_optimization/pal
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policy_optimization/pg
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policy_optimization/ppo
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value_optimization/rainbow
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value_optimization/qr_dqn
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policy_optimization/sac
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policy_optimization/td3
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policy_optimization/wolpertinger
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.. autoclass:: rl_coach.base_parameters.AgentParameters
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.. autoclass:: rl_coach.agents.agent.Agent
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:members:
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:inherited-members:
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