746 research outputs found
Deep learning for video game playing
In this article, we review recent Deep Learning advances in the context of
how they have been applied to play different types of video games such as
first-person shooters, arcade games, and real-time strategy games. We analyze
the unique requirements that different game genres pose to a deep learning
system and highlight important open challenges in the context of applying these
machine learning methods to video games, such as general game playing, dealing
with extremely large decision spaces and sparse rewards
Clyde: A deep reinforcement learning DOOM playing agent
In this paper we present the use of deep reinforcement learn-ing techniques in the context of playing partially observablemulti-agent 3D games. These techniques have traditionallybeen applied to fully observable 2D environments, or navigation tasks in 3D environments. We show the performanceof Clyde in comparison to other competitors within the con-text of the ViZDOOM competition that saw 9 bots competeagainst each other in DOOM death matches. Clyde managedto achieve 3rd place in the ViZDOOM competition held at theIEEE Conference on Computational Intelligence and Games2016. Clyde performed very well considering its relative sim-plicity and the fact that we deliberately avoided a high levelof customisation to keep the algorithm generic
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
Automated Curriculum Learning by Rewarding Temporally Rare Events
Reward shaping allows reinforcement learning (RL) agents to accelerate
learning by receiving additional reward signals. However, these signals can be
difficult to design manually, especially for complex RL tasks. We propose a
simple and general approach that determines the reward of pre-defined events by
their rarity alone. Here events become less rewarding as they are experienced
more often, which encourages the agent to continually explore new types of
events as it learns. The adaptiveness of this reward function results in a form
of automated curriculum learning that does not have to be specified by the
experimenter. We demonstrate that this \emph{Rarity of Events} (RoE) approach
enables the agent to succeed in challenging VizDoom scenarios without access to
the extrinsic reward from the environment. Furthermore, the results demonstrate
that RoE learns a more versatile policy that adapts well to critical changes in
the environment. Rewarding events based on their rarity could help in many
unsolved RL environments that are characterized by sparse extrinsic rewards but
a plethora of known event types.Comment: 8 page
- …