https://github.com/DanielTakeshi/DCUR
Addressing Function Approximation Error in Actor-Critic Methods
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Continuous control in the context of playing games, especially within artificial intelligence (AI) and machine learning (ML), refers to the ability to make a series of smooth, ongoing adjustments or actions to control a game or a simulation. This is in contrast to discrete control, where the actions are limited to a set of specific, distinct choices. Continuous control is crucial in environments where precision, timing, and the magnitude of actions matter, such as driving a car in a racing game, controlling a character in a simulation, or managing the flight of an aircraft in a flight simulator.
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Addressing Function Approximation Error in Actor-Critic Methods
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
ROBEL: Robotics Benchmarks for Learning with Low-Cost Robots
Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research
Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor
Addressing Function Approximation Error in Actor-Critic Methods
Simple random search provides a competitive approach to reinforcement learning
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