def prepareDataContinuousEnvWithReward():
actionSpace = [[0, 1], [1, 0], [-1, 0], [0, -1], [1, 1], [-1, -1], [1, -1],
[-1, 1]]
policy = OptimalPolicy(actionSpace)
import continuousEnv
xbound = [0, 180]
ybound = [0, 180]
vel = 1
transitionFunction = continuousEnv.TransitionFunction(xbound, ybound, vel)
isTerminal = continuousEnv.IsTerminal(vel + .5)
reset = continuousEnv.Reset(xbound, ybound)
maxTimeStep = 180
sampleTraj = SampleTrajectory(maxTimeStep, transitionFunction, isTerminal,
reset)
decay = 0.99
rewardFunction = lambda state, action: -1
accumulateRewards = AccumulateRewards(decay, rewardFunction)
trajNum = 160
path = "./continuous_reward_data.pkl"
generateData(sampleTraj, accumulateRewards, policy, actionSpace, trajNum,
path)
data = loadData(path)
print("{} data points in {}".format(len(data), path))
def prepareDataContinuousEnv():
import continuousEnv as env
xbound = [0, 180]
ybound = [0, 180]
vel = 1
transitionFunction = env.TransitionFunction(xbound, ybound, vel)
isTerminal = env.IsTerminal(vel + .5)
reset = env.Reset(xbound, ybound)
maxTimeStep = 10000
sampleTraj = SampleTrajectory(maxTimeStep, transitionFunction, isTerminal,
reset)
decay = 0.99
rewardFunction = lambda state, action: -1
accumulateRewards = AccumulateRewards(decay, rewardFunction)
policy = env.OptimalPolicy(env.actionSpace)
trajNum = 2000
partialTrajSize = 5
path = "./continuous_data_with_reward.pkl"
data = generateData(sampleTraj,
accumulateRewards,
policy,
env.actionSpace,
trajNum,
path,
withReward=True,
partialTrajSize=partialTrajSize)
print("{} data points in {}".format(len(data), path))
def prepareSheepEscapingEnvData():
import sheepEscapingEnv as env
actionSpace = env.actionSpace
numActionSpace = env.numActionSpace
xBoundary = env.xBoundary
yBoundary = env.yBoundary
vel = env.vel
wolfHeatSeekingPolicy = env.WolfHeatSeekingPolicy(actionSpace)
transition = env.TransitionFunction(xBoundary, yBoundary, vel,
wolfHeatSeekingPolicy)
isTerminal = env.IsTerminal(minDistance=vel + 5)
reset = env.Reset(xBoundary, yBoundary)
rewardFunction = lambda state, action: 1
cInit = 1
cBase = 1
calculateScore = CalculateScore(cInit, cBase)
selectChild = SelectChild(calculateScore)
getActionPrior = UniformActionPrior(actionSpace)
initializeChildren = InitializeChildren(actionSpace, transition,
getActionPrior)
expand = Expand(transition, isTerminal, initializeChildren)
maxRollOutSteps = 10
numSimulations = 600
maxTrajLen = 100
rolloutPolicy = lambda state: actionSpace[np.random.choice(
range(numActionSpace))]
rollout = RollOut(rolloutPolicy, maxRollOutSteps, transition,
rewardFunction, isTerminal)
mcts = MCTSPolicy(numSimulations, selectChild, expand, rollout, backup,
getSoftmaxActionDist)
sampleTraj = SampleTrajectoryWithMCTS(maxTrajLen, isTerminal, reset)
# policy = env.SheepNaiveEscapingPolicy(actionSpace)
# sampleTraj = SampleTrajectory(maxRunningSteps, transition, isTerminal, reset, render=None)
rewardDecay = 0.99
accumulateRewards = AccumulateRewards(rewardDecay, rewardFunction)
trajNum = 500
partialTrajSize = None
path = "./500trajs_sheepEscapingEnv_data_actionDist.pkl"
reportInterval = 10
data = generateData(sampleTraj,
accumulateRewards,
mcts,
actionSpace,
trajNum,
path,
withReward=True,
partialTrajSize=partialTrajSize,
reportInterval=reportInterval)
print("{} data points in {}".format(len(data), path))
def prepareDataContinuousEnv():
actionSpace = [[0, 1], [1, 0], [-1, 0], [0, -1], [1, 1], [-1, -1], [1, -1],
[-1, 1]]
policy = OptimalPolicy(actionSpace)
maxTimeStep = 180
import continuousEnv
xbound = [0, 180]
ybound = [0, 180]
vel = 1
transitionFunction = continuousEnv.TransitionFunction(xbound, ybound, vel)
isTerminal = continuousEnv.IsTerminal(1.5 * vel)
reset = continuousEnv.Reset(xbound, ybound)
sampleTraj = SampleTrajectory(maxTimeStep, transitionFunction, isTerminal,
reset)
trajNum = 1200
path = "./continuous_data.pkl"
generateData(sampleTraj, policy, actionSpace, trajNum, path)
data = loadData(path)
print("{} data points in {}".format(data, path))