Emergence of Locomotion Behaviours in Rich Environments

• Resources
  • Paper
    
    
• Introduction
  • In continuous control tasks, like locomotion, we need to carefully handcraft the reward function
  • Reward engineering is brittle + agent should be able to learn from itself
  • Sensitivity to reward functions = type of overfitting (not generalizable)
  • Paper uses procedurelly generated obstacle courses for agent with different difficulties
    • Acts as implicit curriculum
    • Increases difficulty over time improves learning speed
  • Distributed PPO (DPPO): Builds on top of TRPO and PPO + distributes computation like A3C
• Large scale reinforcement learning with Distributed PPO
  • Robust policy gradients with PPO
    • Use policy gradients with baselines (advantage function)
    • Use trust region constraint that restricts policy update size (via adaptive PPO KL Penalty)
  • Scalable reinforcement learning with Distributed PPO
    • Data collection + gradient calculation distributed across workers
    • Averaging gradients + applying them synchronously leads to better results
    • Use K-step advantage estimation: $\hat{A}_t = \sum _{i=1}^K \gamma^i r _{t+i} + \gamma V _\phi(s _{t+K}) - V _\phi(s_t)$
    • Uses centralized parameter server
    • Workers synchronize their parameters after every gradient step
• Evaluation of Distributed PPO
  • Benchmarked on Planar Walker, Humanoid, and Memory Reacher
  • DPPO achieves similar performance to TRPO but with lower wall clock time.
  • DPPO is faster than A3C in wall clock time
• Methods: environments and models
  • Training environments
    • Rewards
      • Rewards are simple and only signal the agent to make progress (i.e., going forward or not deviating from track)
      • Not carefully fine tuned to achieve certain behavior
    • Terrain / Obstacles
      • Hurdles: Agent needs to jump or climb over
      • Gaps: Agent needs to jump
      • Variable Terrain: Different features like hills, ramps,
      • Slalom Walls: Agent needs to walk around
      • Platform: Agent can jump over or crouch under
      • Trained on different types of courses: single-type, mixtures of single-typed, mixed-terrains, stationary courses, and curriculum courses
    • Observations
      • Agents have sensors on them for proprioceptive and exteroceptive features.
      • Terrain information given too
  • Policy parameterization
    • Two subnetworks
      • One for proprioceptive information
      • One for exteroceptive information
• Results
  • Planar Walker
    • Trained on hurdles, gaps, platforms, and variable terrain on mixed course and mixture of terrains
    • Acquired emergent behaviors spontaneously without fine-tuned reward function
  • Quadruped
    • Trained on variation of hurdles + obstacles that can be avoided and others that require climbing/jumping
    • Traverses reasonably well despite limitations with its body
  • Agents trained with gradually increasing difficulty improves faster than one trained on stationary terrain
  • Next train on flat and challenging courses to see if policies are robust to unobserved variation in the terrain
  • Humanoid
    • Learning is sensitive to the algorithm + many degrees of freedom
    • More variation with results
    • Despite this, they achieve a good policy on diverse terrains
• Discussion
  • Training on richer environments + broader spectrum of tasks = likely improves quality + robustness of learned behaviors.