SlideShare a Scribd company logo
Hosting Reinforcement Learning Challenges on
EvalAI
Deepesh Pathak
Deshraj Yadav
Kartik Verma
Rishabh Bharadwaj
Rishabh Jain
Project Goal
Build feature that will help users to host Reinforcement
Learning Challenges easily on EvalAI and evaluate the agents
at Scale.
The Flow
Submission
1. Participant submits agent image
using EvalAI-CLI, it will Poll API
for various details
2. The Agent image will be pushed to a
registry
3. The EvalAI CLI will send submission
details to API
Manage Submissions
1. The Submission details received at
API will be pushed to Challenge
Specific Queue.
2. RL Submission worker will be polling
this queue and will get details of
new submissions.
RL Worker
Using the Submission details, RL Worker will deploy the logical
entity of environment and agent containers on Kubernetes.
Deployment Logs from rl-worker.py
Polling the API for new
submissions
Processing Submission and
deploying
Deployment
Evaluation
1. Multi Container Policy
2. gRPC for interaction between Agent
and Environment.
Demo
1. A pole is attached by an un-actuated joint
to a cart, which moves along a frictionless
track.
2. The system is controlled by applying a
force of +1 or -1 to the cart. The pendulum
starts upright, and the goal is to prevent
it from falling over.
3. A reward of +1 is provided for every
timestep that the pole remains upright.
Plan for Phase 3
1. Hosting challenges that have environments and agents
coupled tightly. e.g. Habitat Challenge
2. Resource Optimization using GPU sharing
What I learnt?
Next steps after GSoC
Keep Contributing and help
other contributors
Improve EvalAI
Thank You!

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Code Upload Challenge on EvalAI

  • 1. Hosting Reinforcement Learning Challenges on EvalAI Deepesh Pathak Deshraj Yadav Kartik Verma Rishabh Bharadwaj Rishabh Jain
  • 2. Project Goal Build feature that will help users to host Reinforcement Learning Challenges easily on EvalAI and evaluate the agents at Scale.
  • 4. Submission 1. Participant submits agent image using EvalAI-CLI, it will Poll API for various details 2. The Agent image will be pushed to a registry 3. The EvalAI CLI will send submission details to API
  • 5. Manage Submissions 1. The Submission details received at API will be pushed to Challenge Specific Queue. 2. RL Submission worker will be polling this queue and will get details of new submissions.
  • 6. RL Worker Using the Submission details, RL Worker will deploy the logical entity of environment and agent containers on Kubernetes. Deployment Logs from rl-worker.py Polling the API for new submissions Processing Submission and deploying
  • 8. Evaluation 1. Multi Container Policy 2. gRPC for interaction between Agent and Environment.
  • 9. Demo 1. A pole is attached by an un-actuated joint to a cart, which moves along a frictionless track. 2. The system is controlled by applying a force of +1 or -1 to the cart. The pendulum starts upright, and the goal is to prevent it from falling over. 3. A reward of +1 is provided for every timestep that the pole remains upright.
  • 10. Plan for Phase 3 1. Hosting challenges that have environments and agents coupled tightly. e.g. Habitat Challenge 2. Resource Optimization using GPU sharing
  • 12. Next steps after GSoC Keep Contributing and help other contributors Improve EvalAI

Editor's Notes

  1. What is the problem? Other Class of problems not solved by evalai Host RL Types of
  2. What is Env, and what is agent and how they are entities? Challenge creator and participant.
  3. Explain
  4. The Project can be used for Multi Container Policy as well as Single Container Policy. The advantage of Multi Container Policy is that we decouple things that are not related making the approach more generalized. Environment and Agent: Code on https://github.com/vkartik97/rl-grpc Images on https://hub.docker.com/r/vkartik97/agent and https://hub.docker.com/r/vkartik97/env
  5. 1 - example: Habitat
  6. GPU Sharing