91 search results - page 4 / 19 » Parameter-exploring policy gradients |
191
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CEC
2011
IEEE
14 years 5 months ago
2011
IEEE
—Stochastic relaxation aims at finding the minimum of a fitness function by identifying a proper sequence of distributions, in a given model, that minimize the expected value o...
171
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NIPS
2008
15 years 7 months ago
2008
Policy gradient (PG) reinforcement learning algorithms have strong (local) convergence guarantees, but their learning performance is typically limited by a large variance in the e...
160
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IJCAI
2003
15 years 7 months ago
2003
We investigate the problem of non-covariant behavior of policy gradient reinforcement learning algorithms. The policy gradient approach is amenable to analysis by information geom...
127
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AIPS
2007
15 years 7 months ago
2007
We present an any-time concurrent probabilistic temporal planner that includes continuous and discrete uncertainties and metric functions. Our approach is a direct policy search t...
155
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AAAI
2010
15 years 7 months ago
2010
Policy search is a successful approach to reinforcement learning. However, policy improvements often result in the loss of information. Hence, it has been marred by premature conv...