Added a version of CMACs that operates on state feature vectors rather
than the OO-MDP representation. This sacrifices object identifer
invariance for slightly more efficiency.
added additional getters/setters for discount factor
forced value method of ValueFunctionPlanner to return 0 if the input
state is a termainal state.
Now, when relational attributes are created, the associated domain is
automatically set to object identifer dependent.
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updated goal-based rf to also accept terminal functions that are wrapped
into a goal condition
adding a soft inverse time learning rate decay schedule
added support for getting normalized feature object feature vectors and
state feature vectors
added additional VFA accessor features
added LSPI
added feature-wise learning rate polls/peeks
fixed action grounding lookup for parameterized actions
added Fourier basis functions for VFA
updated learning rate functions to take the agent time in the poll to
avoid algorithms like SARSA lambda over polling when they see the same
feature again in an eligibitry trace
Minor changes
adding options to an OOMDPPlaner instance automatically sets the option
to use the planner's hashing factory for computing option transition
dynamics.
Added Sparse Sampling planning algorithm
Updated Q-Value based policies to translate object paramerterized
actions to ensure consistency with the input state.
minor adjustments to sparse sampling
adding addAllRelationalTargets method to ObjectInstance