Solving Atari Games Using Fractals And Entropy

2018-07-03 · Sergio Hernandez Cerezo, Guillem Duran Ballester, Spiros Baxevanakis

In this paper, we introduce a novel MCTS based approach that is derived from the laws of the thermodynamics. The algorithm coined Fractal Monte Carlo (FMC), allows us to create an agent that takes intelligent actions in both continuous and discrete environments while providing control over every aspect of the agent behavior. Results show that FMC is several orders of magnitude more efficient than similar techniques, such as MCTS, in the Atari games tested.

📄 PDF Abstract BibTeX arXiv:1807.01081

Code (1)

FragileTheory/FractalAI 공식 구현

Tasks

Atari Games

Efficient Entropy for Policy Gradient with Multidimensional Action Space

2018-06-02 · Yiming Zhang, Quan Ho Vuong, Kenny Song, Xiao-Yue Gong 외

In recent years, deep reinforcement learning has been shown to be adept at solving sequential decision processes with high-dimensional state spaces such as in the Atari games. Many reinforcement learning problems, howeve…

Atari Games Deep Reinforcement Learning reinforcement-learning Reinforcement Learning+1

On Multi-Agent Learning in Team Sports Games

2019-06-25 · Yunqi Zhao, Igor Borovikov, Jason Rupert, Caedmon Somers 외

In recent years, reinforcement learning has been successful in solving video games from Atari to Star Craft II. However, the end-to-end model-free reinforcement learning (RL) is not sample efficient and requires a signif…

reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Policy Gradient For Multidimensional Action Spaces: Action Sampling and Entropy Bonus

2018-01-01 · ICLR 2018 1 · Vuong Ho Quan, Yiming Zhang, Kenny Song, Xiao-Yue Gong 외

In recent years deep reinforcement learning has been shown to be adept at solving sequential decision processes with high-dimensional state spaces such as in the Atari games. Many reinforcement learning problems, however…

Atari Games Deep Reinforcement Learning reinforcement-learning Reinforcement Learning+1

A Deep Reinforcement Learning Approach for Finding Non-Exploitable Strategies in Two-Player Atari Games

2022-07-18 · Zihan Ding, DiJia Su, Qinghua Liu, Chi Jin

This paper proposes new, end-to-end deep reinforcement learning algorithms for learning two-player zero-sum Markov games. Different from prior efforts on training agents to beat a fixed set of opponents, our objective is…

Atari Games Deep Reinforcement Learning Q-Learning

Target Entropy Annealing for Discrete Soft Actor-Critic

2021-12-06 · Yaosheng Xu, Dailin Hu, Litian Liang, Stephen Mcaleer 외

Soft Actor-Critic (SAC) is considered the state-of-the-art algorithm in continuous action space settings. It uses the maximum entropy framework for efficiency and stability, and applies a heuristic temperature Lagrange t…

Atari Games Scheduling