pyqlearning.deepqlearning package¶
Submodules¶
pyqlearning.deepqlearning.deep_q_network module¶
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class
pyqlearning.deepqlearning.deep_q_network.
DeepQNetwork
(function_approximator)[source]¶ Bases:
pyqlearning.deep_q_learning.DeepQLearning
Abstract base class to implement the Deep Q-Network(DQN).
The structure of Q-Learning is based on the Epsilon Greedy Q-Leanring algorithm, which is a typical off-policy algorithm. In this paradigm, stochastic searching and deterministic searching can coexist by hyperparameter epsilon_greedy_rate that is probability that agent searches greedy. Greedy searching is deterministic in the sensethat policy of agent follows the selection that maximizes the Q-Value.
References
- https://code.accel-brain.com/Reinforcement-Learning/README.html#deep-q-network
- Egorov, M. (2016). Multi-agent deep reinforcement learning.(URL: https://pdfs.semanticscholar.org/dd98/9d94613f439c05725bad958929357e365084.pdf)
- Gupta, J. K., Egorov, M., & Kochenderfer, M. (2017, May). Cooperative multi-agent control using deep reinforcement learning. In International Conference on Autonomous Agents and Multiagent Systems (pp. 66-83). Springer, Cham.
- Mnih, V., Kavukcuoglu, K., Silver, D., Graves, A., Antonoglou, I., Wierstra, D., & Riedmiller, M. (2013). Playing atari with deep reinforcement learning. arXiv preprint arXiv:1312.5602.
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epsilon_greedy_rate
¶ getter
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select_action
(next_action_arr, next_q_arr)[source]¶ Select action by Q(state, action).
Parameters: - next_action_arr – np.ndarray of actions.
- next_q_arr – np.ndarray of Q-Values.
- Retruns:
- Tuple(np.ndarray of action., Q-Value)