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ICML
2009
IEEE
15 years 10 months ago
Model-free reinforcement learning as mixture learning
We cast model-free reinforcement learning as the problem of maximizing the likelihood of a probabilistic mixture model via sampling, addressing both the infinite and finite horizo...
Nikos Vlassis, Marc Toussaint
ATAL
2009
Springer
15 years 4 months ago
An empirical analysis of value function-based and policy search reinforcement learning
In several agent-oriented scenarios in the real world, an autonomous agent that is situated in an unknown environment must learn through a process of trial and error to take actio...
Shivaram Kalyanakrishnan, Peter Stone
AAAI
1997
14 years 11 months ago
The "Inverse Hollywood Problem": From Video to Scripts and Storyboards via Causal Analysis
We address the problem of visually detecting causal events and tting them together into a coherent story of the action witnessed by the camera. We show that this can be done by re...
Matthew Brand
ICML
1995
IEEE
15 years 10 months ago
Residual Algorithms: Reinforcement Learning with Function Approximation
A number of reinforcement learning algorithms have been developed that are guaranteed to converge to the optimal solution when used with lookup tables. It is shown, however, that ...
Leemon C. Baird III
GECCO
2006
Springer
208views Optimization» more  GECCO 2006»
15 years 1 months ago
Comparing evolutionary and temporal difference methods in a reinforcement learning domain
Both genetic algorithms (GAs) and temporal difference (TD) methods have proven effective at solving reinforcement learning (RL) problems. However, since few rigorous empirical com...
Matthew E. Taylor, Shimon Whiteson, Peter Stone