playing-games-10
Control with Prametrised Actions
3260 papers • 126 benchmarks • 313 datasets
Most reinforcement learning research papers focus on environments where the agent’s actions are either discrete or continuous. However, when training an agent to play a video game, it is common to encounter situations where actions have both discrete and continuous components. For example, a set of high-level discrete actions (ex: move, jump, fire), each of them being associated with continuous parameters (ex: target coordinates for the move action, direction for the jump action, aiming angle for the fire action). These kinds of tasks are included in Control with Parameterised Actions.
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Benchmarks
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Trend
Dataset
Best Model
Actions
Platform
Platform
Half Field Offence
Half Field Offence
Robot Soccer Goal
Libraries
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Datasets
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Subtasks
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Most implemented papers
Multi-Pass Q-Networks for Deep Reinforcement Learning with Parameterised Action Spaces
Steven D. James, G. Konidaris, Craig J. Bester•Thu May 09 2019
It is empirically demonstrated that MP-DQN significantly outperforms P-D QN and other previous algorithms in terms of data efficiency and converged policy performance on the Platform, Robot Soccer Goal, and Half Field Offense domains.
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