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About D R L P - A S: EEP Einforcement Earning in Arameter Ized Ction Pace
This paper extends deep reinforcement learning techniques to problems with parameterized action spaces. The authors apply this to the RoboCup soccer Half-Field Offense domain, which has discrete action types (e.g. dash, turn) that each require continuous parameters (e.g. power, direction). The paper modifies the DDPG algorithm to stably learn policies for this domain. Experimental results show the learned agents can reliably score goals, outperforming the previous champion agent. This work successfully expands deep RL to problems with parameterized continuous action spaces.
Detailed Information
| Author: | ['何孟儒'] |
|---|---|
| Publication Year: | 2018 |
| Pages: | 12 |
| Language: | English |
| Format: | |
| Price: | FREE |
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