Our aim has been to design, develop and test a preliminary solution that exploits Proximal Policy Optimization algorithms to train agents without the need of a ...
Our aim has been to design, develop and test a preliminary solution that exploits Proximal Policy Optimization algorithms to train agents without the need of a ...
... Deep reinforcement learning currently has great potential and advantages in solving complex nonlinear control problems such as robotic arm control, ...
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What is reinforcement learning as an approach to train multiplayer first person shooter game agents?
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In the end, results showed that the agents trained with Curriculum Learning achieved better performance than the ones trained with Behaviour Cloning by a matter ...
Mar 5, 2024 · In the end, results showed that the agents trained with Curriculum Learning achieved better performance than the ones trained with Behaviour ...
In this paper, we present the first architecture to tackle 3D environments in first-person shooter games, that involve partially observ- able states. Typically, ...
Typically, deep reinforcement learning methods only utilize visual input for training. We present a method to augment these models to exploit game feature ...
Oct 29, 2018 · The tests showed that the competitive training of autonomous agents leads to a greater number of wins compared to training against non- ...
In this paper, we train agents on the health gathering scenario of the classical first-person shooter game Doom by first presenting the Direct Future ...
Sep 18, 2016 · We show that the proposed architecture substantially outperforms built-in AI agents of the game as well as humans in deathmatch scenarios.
Missing: preliminary results.