SubWorld Dynamic Programming

This repository conatins the code required to create dynamic programming based SubWorld:

• Charts
• Water currents
• Value functions
• Policies

If you use this work, please cite

@article{beeler2021dynamic,
  title={Dynamic programming with partial information to overcome navigational uncertainty in a nautical environment},
  author={Beeler, Chris and Li, Xinkai and Crowley, Mark and Fraser, Maia and Tamblyn, Isaac},
  journal={arXiv preprint arXiv:2112.14657},
  year={2021}
}

The goal of this project is to solve the semi-controlled sensing POMDP SubWorld (more officially referred to as a Nautical Navigation Environment). This environment is a semi-controlled sensing problem because it contains both tradition state-modifying actions in typical POMDPs and non-state modifying information revealing measurement actions in controlled sensing POMDPs.

Released Versions

For specific versions used in published (or submitted) papers, please view the other existing branches.

Version	Paper
v1.0	Beeler, Chris, et al. "Dynamic programming with partial information to overcome navigational uncertainty in a nautical environment." arXiv preprint arXiv:2112.14657 (2021).

Generating Charts

Running gen_land.py will generate a single chart with water currents stored in data/charts/charts_SEED.npz. Running gen_land_many.py will generate many charts with water currents stored in data/charts/charts_SEED.npz. These charts are generated in parallel using multiprocessing.

The charts and water currents can be visualized using plot_chart.py.

Generating Value Functions

Running chart_value.py will generate the value function assuming the chart has no water currents. The value function is generated using Bellman's dynamic programming algorithm and will be stored in data/value/value_SEED.npz. Running chart_value_many.py will generate the value functions for each chart from gen_land_many.py stored in data/value/value_SEED.npz.

The value functions can be visualized using plot_value.py.

Generating Policies

Running chart_policy_gps.py will generate a policy based on the value function. The policy's trajectectory will be stored in data/policy/policy_gps_SEED.npz. Running chart_policy_many.py will generate policies for each value function from chart_value_many.py stored in data/policy/policy_gps_SEED.npz.

The policy trajectories can be visualized using plot_policy.py.

YAML Parameters

The parameters used in each task are stored in params.yaml. Some parameters may carry forward into other tasks.

Generating Charts

Parameter	Type	Description
seed	Non-negative int	The random seed used create the islands that define the chart.
dim	Positive int	The dimension size of the chart.
n_islands	Non-negative int or None	The number of islands that will be generated. Setting None will result in a random number of islands.
min_islands	Non-negative int	The minimum number of islands that will be generated if n_islands is None.
max_islands	Non-negative int	The maximum number of islands that will be generated if n_islands is None.
size	Non-negative float	The size of the x and y dimensions of the chart. The Submarine can move up to 1 unit per action.
min_height	Non-negative float	The minimum height of each island.
max_height	Positive float > min_height	The maximum height of each island.
x_decay_min	Positive float	The minimum decay rate in the x direction of each island.
x_decay_max	Positive float > x_decay_min	The maximum decay rate in the x direction of each island.
y_decay_min	Positive float	The minimum decay rate in the y direction of each island.
y_decay_max	Positive float > y_decay_min	The maximum decay rate in the y direction of each island.
max_cur	Non-negative float	The maximum water current magnitude.

Value Function Generation

Parameter	Type	Description
target_x	Non-negative float < dim or None	The x coordinate of the target.
target_y	Non-negative float < dim or None	The y coordinate of the target.
discount	Non-negative float < 1	The discount factor used in Bellman's dynamic programming algorithm.
n_t	Positive int	The number of discretized throttle actions.
n_h	Positive int	The number of discritized heading actions.
tol	float	Log base 10 of the convergence tolerence used in Bellman's dynamic programming algorithm. I.e. tol=-6 -> a tolerence of 1e-6.

Policy Generation

Parameter	Type	Description
sub_x	Non-negative float < dim or None	The x coordinate for the submarine's starting position. Setting None will result in a random coordinate.
sub_y	Non-negative float < dim or None	The y coordinate for the submarine's starting position. Setting None will result in a random coordinate.
n_steps	Positive int	The maximum number of steps the agent can take before the episode ending.
gps_cost	Non-negative float	The cost required to use the GPS. If set to zero, the GPS will be used at every step.
cur_cost	Non-negative float	The cost required to use the Current Profiler. If set to zero, the Current Profiler will be used at every step.
uncert_pos	Non-negative float	The rate at which uncertainty in position increases.
uncert_cur	Non-negative float	The rate at which uncertainty in water current increases.
uncert_res	Positive int	The number of trajectories simulated for each axis of uncertainty, (x, y) for both position and water current, per action estimate. I.e. uncert_res=3 -> 81 trajectories simulated per action estimate.

Extra Paramters for Parallelization

Parameter	Type	Description
n_cpu	Positive int	The number of CPUs used in multiprocessing.Pool when generating many charts/value functions.
maps_i	Non-negative int	The seed to start at when generating many charts.
maps_f	Positive int > maps_i	The seed to end at when generating many charts.