6,728 research outputs found
Exploiting the Sign of the Advantage Function to Learn Deterministic Policies in Continuous Domains
In the context of learning deterministic policies in continuous domains, we
revisit an approach, which was first proposed in Continuous Actor Critic
Learning Automaton (CACLA) and later extended in Neural Fitted Actor Critic
(NFAC). This approach is based on a policy update different from that of
deterministic policy gradient (DPG). Previous work has observed its excellent
performance empirically, but a theoretical justification is lacking. To fill
this gap, we provide a theoretical explanation to motivate this unorthodox
policy update by relating it to another update and making explicit the
objective function of the latter. We furthermore discuss in depth the
properties of these updates to get a deeper understanding of the overall
approach. In addition, we extend it and propose a new trust region algorithm,
Penalized NFAC (PeNFAC). Finally, we experimentally demonstrate in several
classic control problems that it surpasses the state-of-the-art algorithms to
learn deterministic policies.Comment: International Joint Conferences on Artificial Intelligenc
Exploiting the sign of the advantage function to learn deterministic policies in continuous domains
International audienceIn the context of learning deterministic policies in continuous domains, we revisit an approach, which was first proposed in Continuous Actor Critic Learning Automaton (CACLA) and later extended in Neural Fitted Actor Critic (NFAC). This approach is based on a policy update different from that of deterministic policy gradient (DPG). Previous work has observed its excellent performance empirically, but a theoretical justification is lacking. To fill this gap, we provide a theoretical explanation to motivate this unorthodox policy update by relating it to another update and making explicit the objective function of the latter. We furthermore discuss in depth the properties of these updates to get a deeper understanding of the overall approach. In addition, we extend it and propose a new trust region algorithm, Penalized NFAC (PeNFAC). Finally, we experimentally demonstrate in several classic control problems that it surpasses the state-of-the-art algorithms to learn determinis-tic policies
Deep Variational Reinforcement Learning for POMDPs
Many real-world sequential decision making problems are partially observable
by nature, and the environment model is typically unknown. Consequently, there
is great need for reinforcement learning methods that can tackle such problems
given only a stream of incomplete and noisy observations. In this paper, we
propose deep variational reinforcement learning (DVRL), which introduces an
inductive bias that allows an agent to learn a generative model of the
environment and perform inference in that model to effectively aggregate the
available information. We develop an n-step approximation to the evidence lower
bound (ELBO), allowing the model to be trained jointly with the policy. This
ensures that the latent state representation is suitable for the control task.
In experiments on Mountain Hike and flickering Atari we show that our method
outperforms previous approaches relying on recurrent neural networks to encode
the past
Near Optimal Exploration-Exploitation in Non-Communicating Markov Decision Processes
While designing the state space of an MDP, it is common to include states
that are transient or not reachable by any policy (e.g., in mountain car, the
product space of speed and position contains configurations that are not
physically reachable). This leads to defining weakly-communicating or
multi-chain MDPs. In this paper, we introduce \tucrl, the first algorithm able
to perform efficient exploration-exploitation in any finite Markov Decision
Process (MDP) without requiring any form of prior knowledge. In particular, for
any MDP with communicating states, actions and
possible communicating next states,
we derive a regret bound, where is the diameter
(i.e., the longest shortest path) of the communicating part of the MDP. This is
in contrast with optimistic algorithms (e.g., UCRL, Optimistic PSRL) that
suffer linear regret in weakly-communicating MDPs, as well as posterior
sampling or regularised algorithms (e.g., REGAL), which require prior knowledge
on the bias span of the optimal policy to bias the exploration to achieve
sub-linear regret. We also prove that in weakly-communicating MDPs, no
algorithm can ever achieve a logarithmic growth of the regret without first
suffering a linear regret for a number of steps that is exponential in the
parameters of the MDP. Finally, we report numerical simulations supporting our
theoretical findings and showing how TUCRL overcomes the limitations of the
state-of-the-art
Reinforcement Learning: A Survey
This paper surveys the field of reinforcement learning from a
computer-science perspective. It is written to be accessible to researchers
familiar with machine learning. Both the historical basis of the field and a
broad selection of current work are summarized. Reinforcement learning is the
problem faced by an agent that learns behavior through trial-and-error
interactions with a dynamic environment. The work described here has a
resemblance to work in psychology, but differs considerably in the details and
in the use of the word ``reinforcement.'' The paper discusses central issues of
reinforcement learning, including trading off exploration and exploitation,
establishing the foundations of the field via Markov decision theory, learning
from delayed reinforcement, constructing empirical models to accelerate
learning, making use of generalization and hierarchy, and coping with hidden
state. It concludes with a survey of some implemented systems and an assessment
of the practical utility of current methods for reinforcement learning.Comment: See http://www.jair.org/ for any accompanying file
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