Commit 4f17fb5f authored by Steven Cordwell's avatar Steven Cordwell

update to HISTORY file

parent dc4978c3
v0.9 - the value iteration Gauss-Seidel algorithm is now in working order. The class ValueIterationGS should be stable and usable. Use like this:
>>> import mdp
>>> P, R = mdp.exampleRand(10, 3) # to create a random transition and reward matrices with 10 states and 3 actions
>>> vigs = mdp.ValueIterationGS(P, R, 0.9 # assuming a discount rate of 0.9
>>> vigs.iterate() # to run the algorithm, then type vigs.policy after it has finished to see the optimal policy
v0.8 - The policy iteration algorithm port is now usable. The PolicyIteration class should now be stable. To use:
>>> import mdp
>>> P, R = mdp.exampleForest() # to use the forest example as the transition and reward matrices
......
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