The astonishing success of AlphaGo Zero\cite{Silver_AlphaGo} invokes a worldwide discussion of the future of our human society with a mixed mood of hope, anxiousness, excitement and fear. We try to dymystify AlphaGo Zero by a qualitative analysis to indicate that AlphaGo Zero can be understood as a specially structured…
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16 results for “AlphaGo”
Bayesian optimization improved AlphaGo's win-rate from 50% to 66.5%.
problem Hyper-parameter tuning for machine learning models.
method Bayesian optimization for hyper-parameter tuning.
result Bayesian optimization improved AlphaGo's performance in self-play games.
New method improves solving NP-hard problems on graphs.
problem Solving combinatorial optimization problems on graphs.
method A novel reinforcement learning strategy based on AlphaGo Zero for graph embeddings.
result Our method generalizes better to various graphs than S2V-DQN.
Open-source Go AI achieves perfect record against top professionals.
problem Understanding and usability of AlphaZero algorithms.
method Open-source reimplementation of AlphaZero algorithm.
result Achieves perfect (20:0) record against global top professionals.
Automated framework generates mechanical models via deep reinforcement learning.
problem Deriving theoretical-consistent, micro-structural-based traction-separation laws.
method Meta-modeling framework using deep reinforcement learning to form graph edges and maximize model score.
result Automated model generation outperforms existing cohesive models and detects hidden mechanisms.
New method uses MCTS only at test time for faster game learning.
problem High computational demands in game learning.
method Combines MCTS with TD learning agents for faster, reproducible agents.
result First learning-from-scratch agent to beat Edax up to level 7.
Enhances PMD with lookahead to improve RL performance.
problem Improving RL performance with greedy policies over 1-step.
method Integrates multi-step greedy policies into PMD with lookahead.
result Shows faster convergence rate for -PMD.
Exploring applications of deep reinforcement learning for real-world autonomous driving systemscs.LG
This paper aims to promote real-world use of DRL in autonomous driving.
problem Challenges in deploying DRL in real-world autonomous driving systems.
method Overview of AD tasks, RL algorithms, and DRL applications; discussion of deployment challenges.
result Challenges to real-world deployment of DRL in AD systems.
Achieving superhuman playing level by AlphaGo corroborated the capabilities of convolutional neural architectures (CNNs) for capturing complex spatial patterns. This result was to a great extent due to several analogies between Go board states and 2D images CNNs have been designed for, in particular translational invar…
Recently, a novel class of Approximate Policy Iteration (API) algorithms have demonstrated impressive practical performance (e.g., ExIt from [2], AlphaGo-Zero from [27]). This new family of algorithms maintains, and alternately optimizes, two policies: a fast, reactive policy (e.g., a deep neural network) deployed at t…
A RL approach finds Nash equilibrium for turn-based zero-sum games.
problem Finding Nash equilibrium in two-player turn-based zero-sum games.
method EIS method combining exploration, policy improvement, and supervised learning.
result EIS method finds an ε-approximate value function of Nash equilibrium in O(ε^(-(d+4))) steps.
While many recent advances in deep reinforcement learning (RL) rely on model-free methods, model-based approaches remain an alluring prospect for their potential to exploit unsupervised data to learn environment model. In this work, we provide an extensive study on the design of deep generative models for RL environmen…
This paper applies DRL to mean reversion trading problems.
problem Adopting DRL for financial trading problems.
method Integrates function properties into DRL for mean reversion trading.
result Demonstrates a highly-performant DRL solution for financial decision-making.
Multiplayer Online Battle Arena (MOBA) is currently one of the most popular genres of digital games around the world. The domain of knowledge contained in these complicated games is large. It is hard for humans and algorithms to evaluate the real-time game situation or predict the game result. In this paper, we introdu…
Regret Distribution in Stochastic Bandits: Optimal Trade-off between Expectation and Tail Riskstat.ML
Optimizes regret distribution in stochastic bandits for risk balance.
problem Balancing regret expectation and tail risk in stochastic bandits.
method Characterizes optimal regret tail probability for any threshold, proposes new policies.
result Discovers an intrinsic gap in optimal tail rate based on time horizon uncertainty.
Proposes a self-learning algorithm using reinforcement learning to adapt to data changes.
problem Overcoming concept drift in statistical models over time.
method Reinforcement learning combined with genetic algorithms and Q-learning.
result Algorithm can adapt to new data patterns and self-calibrate.