  We investigate improving Monte Carlo Tree Search based solvers for Partially
Observable Markov Decision Processes (POMDPs), when applied to adaptive
sampling problems. We propose improvements in rollout allocation, the action
exploration algorithm, and plan commitment. The first allocates a different
number of rollouts depending on how many actions the agent has taken in an
episode. We find that rollouts are more valuable after some initial information
is gained about the environment. Thus, a linear increase in the number of
rollouts, i.e. allocating a fixed number at each step, is not appropriate for
adaptive sampling tasks. The second alters which actions the agent chooses to
explore when building the planning tree. We find that by using knowledge of the
number of rollouts allocated, the agent can more effectively choose actions to
explore. The third improvement is in determining how many actions the agent
should take from one plan. Typically, an agent will plan to take the first
action from the planning tree and then call the planner again from the new
state. Using statistical techniques, we show that it is possible to greatly
reduce the number of rollouts by increasing the number of actions taken from a
single planning tree without affecting the agent's final reward. Finally, we
demonstrate experimentally, on simulated and real aquatic data from an
underwater robot, that these improvements can be combined, leading to better
adaptive sampling. The code for this work is available at
https://github.com/uscresl/AdaptiveSamplingPOMCP
