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This project forked from bonetblai/mdp-engine
Automatically exported from code.google.com/p/mdp-engine
Implementation of different algorithms and policies for solving MDPs. MDPs are specified directly in C++ using the supplied template libraries. Algorithms, policies and heuristics are specified as string requests passed to the dispatcher. See examples, like sailing, on how to implement this. Typical requests: Algorithms for solving the MDP form the initial state (all parameters get default values, specify if you want to change): algorithm=value-iteration(epsilon=<float>,max-number-iterations=<integer>,heuristic=<request>,seed=<integer>) algorithm=improved-lao(epsilon=<float>,heuristic=<request>,seed=<integer) algorithm=hdp(epsilon=<float>,heuristic=<request>,seed=<integer) algorithm=ldfs(epsilon=<float>,heuristic=<request>,seed=<integer) algorithm=ldfs-plus(epsilon=<float>,heuristic=<request>,seed=<integer) algorithm=plain-check(epsilon=<float>,heuristic=<request>,seed=<integer) algorithm=lrtdp(epsilon=<float>,heuristic=<request>,epsilon_greedy=<float>,seed=<integer) algorithm=standard-lrtdp(epsilon=<float>,heuristic=<request>,epsilon_greedy=<float>,seed=<integer) algorithm=uniform-lrtdp(epsilon=<float>,heuristic=<request>,epsilon_greedy=<float>,seed=<integer) algorithm=bounded-lrtdp(epsilon=<float>,heuristic=<request>,bound=<integer>,epsilon_greedy=<float>,seed=<integer) algorithm=simple-a*(heuristic=<request>,seed=<integer) // lrtdp is alias for standard-lrtdp // for bounded-lrtdp(), it is not a good idea to use default value for bound // simple-a* is only good for deterministic MDPs (i.e. OR graphs) // Don't assign values to bound and epsilon-greedy unless you know what you are doing. // Default values: seed = 0 epsilon = 0.0 // not a good idea to use this default value heuristic = null // translates into zero estimates max-number-iterations = max bound = max epsilon-greedy = 0.0 // Heuristics (used in algorithms and policies): zero() min-min(algorithm=<request>) optimal(algorithm=<request>) scaled(heuristic=<request>,weight=<float>)) // Parameters for heuristics are mandatory // Policies for online-planning from initial state: policy=random() policy=greedy(optimistic=<boolean>,random-ties=<boolean>,caching=<boolean>,heuristic=<request>) policy=optimal(algorithm=<request>) policy=rollout(width=<integer>,depth=<integer>,nesting=<integer>,policy=<request>) policy=uct(width=<integer>,horizon=<integer>,parameter=<float>,random-ties=<boolean>,policy=<request>) policy=aot(width=<integer>,horizon=<integer>,probability=<float>,expansions-per-iteration=<integer>,random-ties=<boolean>,policy=<request>,heuristic=<request>) policy=finite-horizon-lrtdp(horizon=<integer>,max-trials=<integer>,labeling=<boolean>,random-ties=<boolean>,heuristic=<request>)
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