Control What You Can: Intrinsically Motivated Task-Planning Agent

Blaes et al., 2019

Source: Blaes et al., 2019


  • Addresses how to make an agent learn efficiently to control its environment with minimal external reward
  • Proposes method that combines task-level planning with intrinsic motivation
  • Improved performance compared to intrinsically motivated, non-hierarchical and hierarchical baselines in synthetic and robotic manipulation environments
  • Links: [ website ] [ pdf ]


  • Babies seemingly conduct experiments on the world and analyze the statistics of their observations to form an understanding of their world
  • Undirected “play” behavior is also commonly observed, which can be viewed as trying to gain control
    • One example is learning to use tools to increase what is controllable


  • Assume observable state space is partitioned into potentially controllable components (goal spaces), manipulation of these components is formulated as tasks
  • The perception problem of constructing the goal spaces from sensor modalities (e.g.image data) is not considered
  • Control What You Can (CWYC) approach:
    • Tasks defined by components of the state space
    • Task selector, implemented as multi-armed bandit, selects (final) tasks that maximizes expected learning progress
    • $\epsilon$-greedy task planner computes sub-task sequence form learned task graph, which captures how quickly a sub-task can be solved when another sub-task is performed directly before
    • Sub-goal generator, implemented with relational attention networks, create goals in the current sub-task to maximize success in subsequent task
    • Goal-conditioned task-specific low-level policies control the agent (SAC or DDPG+HER)
    • Training results (success rate, progress, surprise) is stored in a history buffer
    • Intrisic motivation module computes rewards for task selector, task planner, and sub-goal generator
  • Learning progress is defined as the time derivative of the success rate, with success defined as reaching a goal state within some tolerance
  • Use thresholded prediction error to bootstrap early learning in task selector


  • Environments:
    • Synthetic environment: 2-DOF point mass agent with several objects in an enclosed area, implemented in MuJoCo, continuous state and action spaces
      • Contains four objects: tool, heavy object, unreliable (50%) object, and random object
      • Arena is large so random encounters are unlikely
    • Robotic manipulation: robotic arm with gripper (3+1 DOF) in front of table with hook and box at random, out of reach locations, needs to use hook
      • Goal spaces defined as: reaching target position with gipper, manipulating the hook, manipulating the box
      • Objects relations are less obvious, but random manipulations are more frequent
  • Metrics:
    • Success rate: overall success of reaching random goal in each task space
  • Baselines:
    • Hierarchial reinforcement learning with off-policy correction (HIRO): sovles each task independently
    • Intrisic curiosity module with surprise (ICM-S)
    • Intrisic curiosity module with raw prediction error (ICM-E)
    • SAC: low-level controller
    • DDPG+HER: low-level controller
    • CWYC w oracle: oracle task planner and sub-goal generator
  • In synthetic environment, methods that treat each task independently (SAC, ICM-(S/E), HIRO) can only solve locomotion, while CWYC solves all three tasks: locomotion, tool use, and heavy object manipulation
    • Task selector learns curriculum order of locomotion, tool, heavy object, then finally 50% object
  • In the robotic manipulation, only CWYC and DDPG+HER can solve all three tasks
    • CWYC has slightly better sample complexity compared to DDPG+HER
    • Demonstrates that suprise (e.g. unintentionally hitting the tool) helps identify funnel states with ~30 positive samples


  • The success of DDPG+HER in the robotic arm environment makes the results less convincing, this baseline was not used in the synthetic environment
  • State space is relatively low-dimensional and goals are only 2-D, $(x, y)$
  • Parameterization of sub-goal generator seems particularly suited for the specific context
  • Prior information encoded in task/state space limits its practical use
Elias Z. Wang
Elias Z. Wang
AI Researcher | PhD Candidate