1,167 research outputs found
Policy evaluation with temporal differences: a survey and comparison
Policy evaluation is an essential step in most reinforcement learning approaches. It yields a value function, the quality assessment of states for a given policy, which can be used in a policy improvement step. Since the late 1980s, this research area has been dominated by temporal-difference (TD) methods due to their data-efficiency. However, core issues such as stability guarantees in the off-policy scenario, improved sample efficiency and probabilistic treatment of the uncertainty in the estimates have only been tackled recently, which has led to a large number of new approaches.
This paper aims at making these new developments accessible in a concise overview, with foci on underlying cost functions, the off-policy scenario as well as on regularization in high dimensional feature spaces. By presenting the first extensive, systematic comparative evaluations comparing TD, LSTD, LSPE, FPKF, the residual- gradient algorithm, Bellman residual minimization, GTD, GTD2 and TDC, we shed light on the strengths and weaknesses of the methods. Moreover, we present alternative versions of LSTD and LSPE with drastically improved off-policy performance
Planning for Decentralized Control of Multiple Robots Under Uncertainty
We describe a probabilistic framework for synthesizing control policies for
general multi-robot systems, given environment and sensor models and a cost
function. Decentralized, partially observable Markov decision processes
(Dec-POMDPs) are a general model of decision processes where a team of agents
must cooperate to optimize some objective (specified by a shared reward or cost
function) in the presence of uncertainty, but where communication limitations
mean that the agents cannot share their state, so execution must proceed in a
decentralized fashion. While Dec-POMDPs are typically intractable to solve for
real-world problems, recent research on the use of macro-actions in Dec-POMDPs
has significantly increased the size of problem that can be practically solved
as a Dec-POMDP. We describe this general model, and show how, in contrast to
most existing methods that are specialized to a particular problem class, it
can synthesize control policies that use whatever opportunities for
coordination are present in the problem, while balancing off uncertainty in
outcomes, sensor information, and information about other agents. We use three
variations on a warehouse task to show that a single planner of this type can
generate cooperative behavior using task allocation, direct communication, and
signaling, as appropriate
CrossNorm: Normalization for Off-Policy TD Reinforcement Learning
Off-policy temporal difference (TD) methods are a powerful class of
reinforcement learning (RL) algorithms. Intriguingly, deep off-policy TD
algorithms are not commonly used in combination with feature normalization
techniques, despite positive effects of normalization in other domains. We show
that naive application of existing normalization techniques is indeed not
effective, but that well-designed normalization improves optimization stability
and removes the necessity of target networks. In particular, we introduce a
normalization based on a mixture of on- and off-policy transitions, which we
call cross-normalization. It can be regarded as an extension of batch
normalization that re-centers data for two different distributions, as present
in off-policy learning. Applied to DDPG and TD3, cross-normalization improves
over the state of the art across a range of MuJoCo benchmark tasks
- …