10,606 research outputs found
The Hanabi Challenge: A New Frontier for AI Research
From the early days of computing, games have been important testbeds for
studying how well machines can do sophisticated decision making. In recent
years, machine learning has made dramatic advances with artificial agents
reaching superhuman performance in challenge domains like Go, Atari, and some
variants of poker. As with their predecessors of chess, checkers, and
backgammon, these game domains have driven research by providing sophisticated
yet well-defined challenges for artificial intelligence practitioners. We
continue this tradition by proposing the game of Hanabi as a new challenge
domain with novel problems that arise from its combination of purely
cooperative gameplay with two to five players and imperfect information. In
particular, we argue that Hanabi elevates reasoning about the beliefs and
intentions of other agents to the foreground. We believe developing novel
techniques for such theory of mind reasoning will not only be crucial for
success in Hanabi, but also in broader collaborative efforts, especially those
with human partners. To facilitate future research, we introduce the
open-source Hanabi Learning Environment, propose an experimental framework for
the research community to evaluate algorithmic advances, and assess the
performance of current state-of-the-art techniques.Comment: 32 pages, 5 figures, In Press (Artificial Intelligence
Variance Reduction in Monte Carlo Counterfactual Regret Minimization (VR-MCCFR) for Extensive Form Games using Baselines
Learning strategies for imperfect information games from samples of
interaction is a challenging problem. A common method for this setting, Monte
Carlo Counterfactual Regret Minimization (MCCFR), can have slow long-term
convergence rates due to high variance. In this paper, we introduce a variance
reduction technique (VR-MCCFR) that applies to any sampling variant of MCCFR.
Using this technique, per-iteration estimated values and updates are
reformulated as a function of sampled values and state-action baselines,
similar to their use in policy gradient reinforcement learning. The new
formulation allows estimates to be bootstrapped from other estimates within the
same episode, propagating the benefits of baselines along the sampled
trajectory; the estimates remain unbiased even when bootstrapping from other
estimates. Finally, we show that given a perfect baseline, the variance of the
value estimates can be reduced to zero. Experimental evaluation shows that
VR-MCCFR brings an order of magnitude speedup, while the empirical variance
decreases by three orders of magnitude. The decreased variance allows for the
first time CFR+ to be used with sampling, increasing the speedup to two orders
of magnitude
A Survey of Monte Carlo Tree Search Methods
Monte Carlo tree search (MCTS) is a recently proposed search method that combines the precision of tree search with the generality of random sampling. It has received considerable interest due to its spectacular success in the difficult problem of computer Go, but has also proved beneficial in a range of other domains. This paper is a survey of the literature to date, intended to provide a snapshot of the state of the art after the first five years of MCTS research. We outline the core algorithm's derivation, impart some structure on the many variations and enhancements that have been proposed, and summarize the results from the key game and nongame domains to which MCTS methods have been applied. A number of open research questions indicate that the field is ripe for future work
Penalty-regulated dynamics and robust learning procedures in games
Starting from a heuristic learning scheme for N-person games, we derive a new
class of continuous-time learning dynamics consisting of a replicator-like
drift adjusted by a penalty term that renders the boundary of the game's
strategy space repelling. These penalty-regulated dynamics are equivalent to
players keeping an exponentially discounted aggregate of their on-going payoffs
and then using a smooth best response to pick an action based on these
performance scores. Owing to this inherent duality, the proposed dynamics
satisfy a variant of the folk theorem of evolutionary game theory and they
converge to (arbitrarily precise) approximations of Nash equilibria in
potential games. Motivated by applications to traffic engineering, we exploit
this duality further to design a discrete-time, payoff-based learning algorithm
which retains these convergence properties and only requires players to observe
their in-game payoffs: moreover, the algorithm remains robust in the presence
of stochastic perturbations and observation errors, and it does not require any
synchronization between players.Comment: 33 pages, 3 figure
Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games
Many artificial intelligence (AI) applications often require multiple
intelligent agents to work in a collaborative effort. Efficient learning for
intra-agent communication and coordination is an indispensable step towards
general AI. In this paper, we take StarCraft combat game as a case study, where
the task is to coordinate multiple agents as a team to defeat their enemies. To
maintain a scalable yet effective communication protocol, we introduce a
Multiagent Bidirectionally-Coordinated Network (BiCNet ['bIknet]) with a
vectorised extension of actor-critic formulation. We show that BiCNet can
handle different types of combats with arbitrary numbers of AI agents for both
sides. Our analysis demonstrates that without any supervisions such as human
demonstrations or labelled data, BiCNet could learn various types of advanced
coordination strategies that have been commonly used by experienced game
players. In our experiments, we evaluate our approach against multiple
baselines under different scenarios; it shows state-of-the-art performance, and
possesses potential values for large-scale real-world applications.Comment: 10 pages, 10 figures. Previously as title: "Multiagent
Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat
Games", Mar 201
An Exploration of Multi-agent Learning Within the Game of Sheephead
In this paper, we examine a machine learning technique presented by Ishii et al. used to allow for learning in a multi-agent environment and apply an adaptation of this learning technique to the card game Sheephead. We then evaluate the effectiveness of our adaptation by running simulations against rule-based opponents. Multi-agent learning presents several layers of complexity on top of a single-agent learning in a stationary environment. This added complexity and increased state space is just beginning to be addressed by researchers. We utilize techniques used by Ishii et al. to facilitate this multi-agent learning. We model the environment of Sheephead as a partially observable Markov decision process (POMDP). This model will allow us to estimate the hidden state information inherent within the game of Sheephead. By first estimating this information, we restore the Markov property needed to model the problem as a Markov decison problem. We then solve the problem as Ishii et al. did by using a reinforcement learning technique based on the actor-critic algorithm. Though our results were positive, they were skewed by a rules-based implementation of part of the algorithm. Future research will be needed complete this implementation via a learning-based action predictor. Future research should also include testing against human subjects thus removing the rules-based bias inherent in the current algorithm. Given increased processing power, disk space, and improved AI techniques such as the techniques described above, complex multi-agent learning problems which once proved difficult may find solutions from the AI world
ViZDoom Competitions: Playing Doom from Pixels
This paper presents the first two editions of Visual Doom AI Competition,
held in 2016 and 2017. The challenge was to create bots that compete in a
multi-player deathmatch in a first-person shooter (FPS) game, Doom. The bots
had to make their decisions based solely on visual information, i.e., a raw
screen buffer. To play well, the bots needed to understand their surroundings,
navigate, explore, and handle the opponents at the same time. These aspects,
together with the competitive multi-agent aspect of the game, make the
competition a unique platform for evaluating the state of the art reinforcement
learning algorithms. The paper discusses the rules, solutions, results, and
statistics that give insight into the agents' behaviors. Best-performing agents
are described in more detail. The results of the competition lead to the
conclusion that, although reinforcement learning can produce capable Doom bots,
they still are not yet able to successfully compete against humans in this
game. The paper also revisits the ViZDoom environment, which is a flexible,
easy to use, and efficient 3D platform for research for vision-based
reinforcement learning, based on a well-recognized first-person perspective
game Doom
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