18,053 research outputs found
Mega-Reward: Achieving Human-Level Play without Extrinsic Rewards
Intrinsic rewards were introduced to simulate how human intelligence works;
they are usually evaluated by intrinsically-motivated play, i.e., playing games
without extrinsic rewards but evaluated with extrinsic rewards. However, none
of the existing intrinsic reward approaches can achieve human-level performance
under this very challenging setting of intrinsically-motivated play. In this
work, we propose a novel megalomania-driven intrinsic reward (called
mega-reward), which, to our knowledge, is the first approach that achieves
human-level performance in intrinsically-motivated play. Intuitively,
mega-reward comes from the observation that infants' intelligence develops when
they try to gain more control on entities in an environment; therefore,
mega-reward aims to maximize the control capabilities of agents on given
entities in a given environment. To formalize mega-reward, a relational
transition model is proposed to bridge the gaps between direct and latent
control. Experimental studies show that mega-reward (i) can greatly outperform
all state-of-the-art intrinsic reward approaches, (ii) generally achieves the
same level of performance as Ex-PPO and professional human-level scores, and
(iii) has also a superior performance when it is incorporated with extrinsic
rewards
Progressive growing of self-organized hierarchical representations for exploration
Designing agent that can autonomously discover and learn a diversity of
structures and skills in unknown changing environments is key for lifelong
machine learning. A central challenge is how to learn incrementally
representations in order to progressively build a map of the discovered
structures and re-use it to further explore. To address this challenge, we
identify and target several key functionalities. First, we aim to build lasting
representations and avoid catastrophic forgetting throughout the exploration
process. Secondly we aim to learn a diversity of representations allowing to
discover a "diversity of diversity" of structures (and associated skills) in
complex high-dimensional environments. Thirdly, we target representations that
can structure the agent discoveries in a coarse-to-fine manner. Finally, we
target the reuse of such representations to drive exploration toward an
"interesting" type of diversity, for instance leveraging human guidance.
Current approaches in state representation learning rely generally on
monolithic architectures which do not enable all these functionalities.
Therefore, we present a novel technique to progressively construct a Hierarchy
of Observation Latent Models for Exploration Stratification, called HOLMES.
This technique couples the use of a dynamic modular model architecture for
representation learning with intrinsically-motivated goal exploration processes
(IMGEPs). The paper shows results in the domain of automated discovery of
diverse self-organized patterns, considering as testbed the experimental
framework from Reinke et al. (2019)
Higher coordination with less control - A result of information maximization in the sensorimotor loop
This work presents a novel learning method in the context of embodied
artificial intelligence and self-organization, which has as few assumptions and
restrictions as possible about the world and the underlying model. The learning
rule is derived from the principle of maximizing the predictive information in
the sensorimotor loop. It is evaluated on robot chains of varying length with
individually controlled, non-communicating segments. The comparison of the
results shows that maximizing the predictive information per wheel leads to a
higher coordinated behavior of the physically connected robots compared to a
maximization per robot. Another focus of this paper is the analysis of the
effect of the robot chain length on the overall behavior of the robots. It will
be shown that longer chains with less capable controllers outperform those of
shorter length and more complex controllers. The reason is found and discussed
in the information-geometric interpretation of the learning process
Linear combination of one-step predictive information with an external reward in an episodic policy gradient setting: a critical analysis
One of the main challenges in the field of embodied artificial intelligence
is the open-ended autonomous learning of complex behaviours. Our approach is to
use task-independent, information-driven intrinsic motivation(s) to support
task-dependent learning. The work presented here is a preliminary step in which
we investigate the predictive information (the mutual information of the past
and future of the sensor stream) as an intrinsic drive, ideally supporting any
kind of task acquisition. Previous experiments have shown that the predictive
information (PI) is a good candidate to support autonomous, open-ended learning
of complex behaviours, because a maximisation of the PI corresponds to an
exploration of morphology- and environment-dependent behavioural regularities.
The idea is that these regularities can then be exploited in order to solve any
given task. Three different experiments are presented and their results lead to
the conclusion that the linear combination of the one-step PI with an external
reward function is not generally recommended in an episodic policy gradient
setting. Only for hard tasks a great speed-up can be achieved at the cost of an
asymptotic performance lost
Empowerment for Continuous Agent-Environment Systems
This paper develops generalizations of empowerment to continuous states.
Empowerment is a recently introduced information-theoretic quantity motivated
by hypotheses about the efficiency of the sensorimotor loop in biological
organisms, but also from considerations stemming from curiosity-driven
learning. Empowemerment measures, for agent-environment systems with stochastic
transitions, how much influence an agent has on its environment, but only that
influence that can be sensed by the agent sensors. It is an
information-theoretic generalization of joint controllability (influence on
environment) and observability (measurement by sensors) of the environment by
the agent, both controllability and observability being usually defined in
control theory as the dimensionality of the control/observation spaces. Earlier
work has shown that empowerment has various interesting and relevant
properties, e.g., it allows us to identify salient states using only the
dynamics, and it can act as intrinsic reward without requiring an external
reward. However, in this previous work empowerment was limited to the case of
small-scale and discrete domains and furthermore state transition probabilities
were assumed to be known. The goal of this paper is to extend empowerment to
the significantly more important and relevant case of continuous vector-valued
state spaces and initially unknown state transition probabilities. The
continuous state space is addressed by Monte-Carlo approximation; the unknown
transitions are addressed by model learning and prediction for which we apply
Gaussian processes regression with iterated forecasting. In a number of
well-known continuous control tasks we examine the dynamics induced by
empowerment and include an application to exploration and online model
learning
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