• Corpus ID: 226278317

The Value Equivalence Principle for Model-Based Reinforcement Learning

@article{Grimm2020TheVE,
  title={The Value Equivalence Principle for Model-Based Reinforcement Learning},
  author={Christopher Grimm and Andr{\'e} Barreto and Satinder Singh and David Silver},
  journal={ArXiv},
  year={2020},
  volume={abs/2011.03506}
}
Learning models of the environment from data is often viewed as an essential component to building intelligent reinforcement learning (RL) agents. The common practice is to separate the learning of the model from its use, by constructing a model of the environment's dynamics that correctly predicts the observed state transitions. In this paper we argue that the limited representational resources of model-based RL agents are better used to build models that are directly useful for value-based… 

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References

SHOWING 1-10 OF 51 REFERENCES

The Value-Improvement Path: Towards Better Representations for Reinforcement Learning

This paper argues that the value prediction problems faced by an RL agent should be addressed in isolation, but rather as a single, holistic, prediction problem, and demonstrates that a representation that spans the past value-improvement path will also provide an accurate value approximation for future policy improvements.

Policy-Aware Model Learning for Policy Gradient Methods

This paper examines how the planning module of an MBRL algorithm uses the model, and proposes that the model learning module should incorporate the way the planner is going to use the model.

SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning

This paper presents a method for learning representations that are suitable for iterative model-based policy improvement, even when the underlying dynamical system has complex dynamics and image observations, in that these representations are optimized for inferring simple dynamics and cost models given data from the current policy.

Model-Based Reinforcement Learning with Value-Targeted Regression

This paper proposes a model based RL algorithm that is based on optimism principle, and derives a bound on the regret, which is independent of the total number of states or actions, and is close to a lower bound $\Omega(\sqrt{HdT})$.

TreeQN and ATreeC: Differentiable Tree-Structured Models for Deep Reinforcement Learning

TreeQN, a differentiable, recursive, tree-structured model that serves as a drop-in replacement for any value function network in deep RL with discrete actions, and ATreeC, an actor-critic variant that augments TreeQN with a softmax layer to form a stochastic policy network.

Reinforcement learning with misspecified model classes

An algorithm is presented for which the highest performing model from the model class is guaranteed to be found given unlimited data and computation, by explicitly selecting the model which achieves the highest expected reward, rather than the most likely model.

Iterative Value-Aware Model Learning

A new model-based reinforcement learning (MBRL) framework that incorporates the underlying decision problem in learning the transition model of the environment, called Iterative VAML, that benefits from the structure of how the planning is performed (i.e., through approximate value iteration) to devise a simpler optimization problem.

Algorithms for Reinforcement Learning

This book focuses on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming, and gives a fairly comprehensive catalog of learning problems, and describes the core ideas, followed by the discussion of their theoretical properties and limitations.

Value-Aware Loss Function for Model-based Reinforcement Learning

This work argues that estimating a generative model that minimizes a probabilistic loss, such as the log-loss, is an overkill because it does not take into account the underlying structure of decision problem and the RL algorithm that intends to solve it.

Dyna-Style Planning with Linear Function Approximation and Prioritized Sweeping

This paper develops an explicitly model-based approach extending the Dyna architecture to linear function approximation, to prove that linear Dyna-style planning converges to a unique solution independent of the generating distribution, under natural conditions.
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