gym-po-taxi

Partially-observable taxi environment, with internal vectorization

Links to look at for own implementation

Gymnax: Classic control, bsuite, MinAtar, FourRooms, MetaMaze, PointRobot, Bandits in JAX. Supports Podracer architecture

• Most interesting environments are probably MemoryChain, FourRooms, MetaMaze, PointRobot

ROOMS and C-ROOMS: ROOMS and C-ROOMs for reference

• Velocity-based vs just position
• Fixed layouts ahead of time. Random agent spawn. Fixed or set or random goal
• Discrete action (8 or 4 cardinal directions) vs Continuous (2D)
  • 2 forms of action failure. 0.2 chance of taking random action (cardinal) or flipping signs (continuous). 0.2 standard deviation for Gaussian movement
• What to do for walls?
  • Discrete case is easy. Don't move.
  • Continuous case could be the same. Alternatively, draw the vector, stop right at wall.
• Observation?
  • Non-continuous:
    • Fully observable: grid discrete state. Goal state if random?
    • Partially observable: 4D Hansen (adjacent), 8D Hansen, nxn grid
  • Continuous:
    • Fully observable: (x,y) coordinate, Need (dx, dy) if velocity-based. Goal state if random?
    • Partially observable:
      • (x,y) w/o velocity, (x,y) downsampled to grid
      • 4/8D Hansen (0/1 walls in range 1M), 4/8D walls (distance of closest wall)

Pocman/Pacman: Fully/partially-observable pocman from POMCP

Battleship: Partially observable battleship

Rocksample: Also has battleship

Isaacverse: GPU physics control

Mo-Gym: Multi-objective. Fancy fourrooms, reacher with more objectives,

gym-sokoban: pixel-based though...

CARL: Context-adaptive RL, reconfigure envs (Mario, Brax, control)

highway-env: Must infer behaviors of others

Other

• SpaceRobot: Non-actuated base space robot
• Learn2Race: Needs GPU. Eh...
• tmrl: TrackMania racing, 19-D LIDAR option
• ShinRL: Future reference, interesting