83 research outputs found
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
Sample-Efficient Co-Design of Robotic Agents Using Multi-fidelity Training on Universal Policy Network
Co-design involves simultaneously optimizing the controller and agents
physical design. Its inherent bi-level optimization formulation necessitates an
outer loop design optimization driven by an inner loop control optimization.
This can be challenging when the design space is large and each design
evaluation involves data-intensive reinforcement learning process for control
optimization. To improve the sample-efficiency we propose a
multi-fidelity-based design exploration strategy based on Hyperband where we
tie the controllers learnt across the design spaces through a universal policy
learner for warm-starting the subsequent controller learning problems. Further,
we recommend a particular way of traversing the Hyperband generated design
matrix that ensures that the stochasticity of the Hyperband is reduced the most
with the increasing warm starting effect of the universal policy learner as it
is strengthened with each new design evaluation. Experiments performed on a
wide range of agent design problems demonstrate the superiority of our method
compared to the baselines. Additionally, analysis of the optimized designs
shows interesting design alterations including design simplifications and
non-intuitive alterations that have emerged in the biological world.Comment: 17 pages, 10 figure
Echo state model of non-Markovian reinforcement learning, An
Department Head: Dale H. Grit.2008 Spring.Includes bibliographical references (pages 137-142).There exists a growing need for intelligent, autonomous control strategies that operate in real-world domains. Theoretically the state-action space must exhibit the Markov property in order for reinforcement learning to be applicable. Empirical evidence, however, suggests that reinforcement learning also applies to domains where the state-action space is approximately Markovian, a requirement for the overwhelming majority of real-world domains. These domains, termed non-Markovian reinforcement learning domains, raise a unique set of practical challenges. The reconstruction dimension required to approximate a Markovian state-space is unknown a priori and can potentially be large. Further, spatial complexity of local function approximation of the reinforcement learning domain grows exponentially with the reconstruction dimension. Parameterized dynamic systems alleviate both embedding length and state-space dimensionality concerns by reconstructing an approximate Markovian state-space via a compact, recurrent representation. Yet this representation extracts a cost; modeling reinforcement learning domains via adaptive, parameterized dynamic systems is characterized by instability, slow-convergence, and high computational or spatial training complexity. The objectives of this research are to demonstrate a stable, convergent, accurate, and scalable model of non-Markovian reinforcement learning domains. These objectives are fulfilled via fixed point analysis of the dynamics underlying the reinforcement learning domain and the Echo State Network, a class of parameterized dynamic system. Understanding models of non-Markovian reinforcement learning domains requires understanding the interactions between learning domains and their models. Fixed point analysis of the Mountain Car Problem reinforcement learning domain, for both local and nonlocal function approximations, suggests a close relationship between the locality of the approximation and the number and severity of bifurcations of the fixed point structure. This research suggests the likely cause of this relationship: reinforcement learning domains exist within a dynamic feature space in which trajectories are analogous to states. The fixed point structure maps dynamic space onto state-space. This explanation suggests two testable hypotheses. Reinforcement learning is sensitive to state-space locality because states cluster as trajectories in time rather than space. Second, models using trajectory-based features should exhibit good modeling performance and few changes in fixed point structure. Analysis of performance of lookup table, feedforward neural network, and Echo State Network (ESN) on the Mountain Car Problem reinforcement learning domain confirm these hypotheses. The ESN is a large, sparse, randomly-generated, unadapted recurrent neural network, which adapts a linear projection of the target domain onto the hidden layer. ESN modeling results on reinforcement learning domains show it achieves performance comparable to lookup table and neural network architectures on the Mountain Car Problem with minimal changes to fixed point structure. Also, the ESN achieves lookup table caliber performance when modeling Acrobot, a four-dimensional control problem, but is less successful modeling the lower dimensional Modified Mountain Car Problem. These performance discrepancies are attributed to the ESN’s excellent ability to represent complex short term dynamics, and its inability to consolidate long temporal dependencies into a static memory. Without memory consolidation, reinforcement learning domains exhibiting attractors with multiple dynamic scales are unlikely to be well-modeled via ESN. To mediate this problem, a simple ESN memory consolidation method is presented and tested for stationary dynamic systems. These results indicate the potential to improve modeling performance in reinforcement learning domains via memory consolidation
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