718 research outputs found
An Adversarial Interpretation of Information-Theoretic Bounded Rationality
Recently, there has been a growing interest in modeling planning with
information constraints. Accordingly, an agent maximizes a regularized expected
utility known as the free energy, where the regularizer is given by the
information divergence from a prior to a posterior policy. While this approach
can be justified in various ways, including from statistical mechanics and
information theory, it is still unclear how it relates to decision-making
against adversarial environments. This connection has previously been suggested
in work relating the free energy to risk-sensitive control and to extensive
form games. Here, we show that a single-agent free energy optimization is
equivalent to a game between the agent and an imaginary adversary. The
adversary can, by paying an exponential penalty, generate costs that diminish
the decision maker's payoffs. It turns out that the optimal strategy of the
adversary consists in choosing costs so as to render the decision maker
indifferent among its choices, which is a definining property of a Nash
equilibrium, thus tightening the connection between free energy optimization
and game theory.Comment: 7 pages, 4 figures. Proceedings of AAAI-1
Quantum Deep Hedging
Quantum machine learning has the potential for a transformative impact across
industry sectors and in particular in finance. In our work we look at the
problem of hedging where deep reinforcement learning offers a powerful
framework for real markets. We develop quantum reinforcement learning methods
based on policy-search and distributional actor-critic algorithms that use
quantum neural network architectures with orthogonal and compound layers for
the policy and value functions. We prove that the quantum neural networks we
use are trainable, and we perform extensive simulations that show that quantum
models can reduce the number of trainable parameters while achieving comparable
performance and that the distributional approach obtains better performance
than other standard approaches, both classical and quantum. We successfully
implement the proposed models on a trapped-ion quantum processor, utilizing
circuits with up to qubits, and observe performance that agrees well with
noiseless simulation. Our quantum techniques are general and can be applied to
other reinforcement learning problems beyond hedging
Reinforcement learning in large state action spaces
Reinforcement learning (RL) is a promising framework for training intelligent agents which learn to optimize long term utility by directly interacting with the environment. Creating RL methods which scale to large state-action spaces is a critical problem towards ensuring real world deployment of RL systems. However, several challenges limit the applicability of RL to large scale settings. These include difficulties with exploration, low sample efficiency, computational intractability, task constraints like decentralization and lack of guarantees about important properties like performance, generalization and robustness in potentially unseen scenarios.
This thesis is motivated towards bridging the aforementioned gap. We propose several principled algorithms and frameworks for studying and addressing the above challenges RL. The proposed methods cover a wide range of RL settings (single and multi-agent systems (MAS) with all the variations in the latter, prediction and control, model-based and model-free methods, value-based and policy-based methods). In this work we propose the first results on several different problems: e.g. tensorization of the Bellman equation which allows exponential sample efficiency gains (Chapter 4), provable suboptimality arising from structural constraints in MAS(Chapter 3), combinatorial generalization results in cooperative MAS(Chapter 5), generalization results on observation shifts(Chapter 7), learning deterministic policies in a probabilistic RL framework(Chapter 6). Our algorithms exhibit provably enhanced performance and sample efficiency along with better scalability. Additionally, we also shed light on generalization aspects of the agents under different frameworks. These properties have been been driven by the use of several advanced tools (e.g. statistical machine learning, state abstraction, variational inference, tensor theory).
In summary, the contributions in this thesis significantly advance progress towards making RL agents ready for large scale, real world applications
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