PARAMETRIZED DEEP Q-NETWORKS LEARNING: PLAYING ONLINE BATTLE ARENA WITH DISCRETE-CONTINUOUS HYBRID ACTION SPACE
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Summary
This paper proposes a parametrized deep Q-network (P-DQN) farmework for the hybrid action space without approximation or relaxation and can be viewed as an extension of the DQN to hybrid actions.
- Type
- article
- Published
- 2018-02-15
- Cited by
- 2
- References
- 35
- OpenAlex
- https://openalex.org/W2787427976
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:27592197
Keywords
Computer science, Reinforcement learning, Action (physics), Set (abstract data type), Space (punctuation)
References
- Stochastic Approximation and Recursive Algorithms and Applications
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- A Stochastic Approximation Method
- Algorithms for Reinforcement Learning
- Stochastic approximation with two time scales
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- A Natural Policy Gradient
- Human-level control through deep reinforcement learning
- The Arcade Learning Environment: An Evaluation Platform for General Agents
- Policy Gradient Methods for Reinforcement Learning with Function Approximation
- Deep Reinforcement Learning with Double Q-Learning
- Deterministic Policy Gradient Algorithms
- Dueling Network Architectures for Deep Reinforcement Learning
- Reinforcement Learning with Parameterized Actions
- Mastering the game of Go with deep neural networks and tree search
- Guided Policy Search as Approximate Mirror Descent
- Playing FPS Games with Deep Reinforcement Learning
- PGQ: Combining policy gradient and Q-learning
- Playing Doom with SLAM-Augmented Deep Reinforcement Learning
- Stabilising Experience Replay for Deep Multi-Agent Reinforcement Learning
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