Search Results for author: Chris Bamford

Found 6 papers, 4 papers with code

Gym-$μ$RTS: Toward Affordable Full Game Real-time Strategy Games Research with Deep Reinforcement Learning

4 code implementations21 May 2021 Shengyi Huang, Santiago Ontañón, Chris Bamford, Lukasz Grela

In recent years, researchers have achieved great success in applying Deep Reinforcement Learning (DRL) algorithms to Real-time Strategy (RTS) games, creating strong autonomous agents that could defeat professional players in StarCraft~II.

reinforcement-learning Reinforcement Learning (RL) +2

Griddly: A platform for AI research in games

no code implementations12 Nov 2020 Chris Bamford, Shengyi Huang, Simon Lucas

In recent years, there have been immense breakthroughs in Game AI research, particularly with Reinforcement Learning (RL).

Reinforcement Learning (RL)

Neural Game Engine: Accurate learning of generalizable forward models from pixels

1 code implementation23 Mar 2020 Chris Bamford, Simon Lucas

Access to a fast and easily copied forward model of a game is essential for model-based reinforcement learning and for algorithms such as Monte Carlo tree search, and is also beneficial as a source of unlimited experience data for model-free algorithms.

Model-based Reinforcement Learning OpenAI Gym

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