🚀 Starter Kit | Getting Started with SageMaker | 🛠️ How to debug your submissions
🏛 Procgen Townhall
Procgen Benchmark is a suite of
16 procedurally-generated gym environments designed to benchmark both sample efficiency and generalization in reinforcement learning. In this competition, participants will attempt to maximize agents' performance using a fixed number of environment interactions. Agents will be evaluated in each of these 16 publicly released environments, as well as in four secret test environments created specifically for this competition. By aggregating performance across so many diverse environments, we can obtain high quality metrics to judge the underlying algorithms.
Since all content is procedurally generated, each Procgen environment intrinsically requires agents to generalize to never-before-seen situations. These environments therefore provide a robust test of an agent's ability to learn in many diverse settings. Moreover, Procgen environments are designed to be lightweight and simple to use. Participants with limited computational resources will be able to easily reproduce baseline results and run new experiments. More details about the design principles and details of individual environments can be found in the paper Leveraging Procedural Generation to Benchmark Reinforcement Learning. Once the competition concludes, all four test environments will be publicly released.
In all rounds, participants will be allotted
8 million timesteps in each environment to train their agents. When evaluating generalization, we will provide participants 200 levels from each environment during the training phase. Participants will also be restricted to no more than 2 hours of compute per environment, using a
V100 GPU and
Participants are expected to operate in good faith and to not attempt to circumvent these restrictions.
Participants will train separate agents for each environment, with the number of environments varying in each round of the competition. In general, performance will be judged by the mean of the normalized returns across environments. In each environment, the normalized return is defined as :
- is the raw expected return
- and are constants chosen (per environment) to approximately bound .
It is possible to choose these constants because each Procgen environment has a clear score ceiling. Using this definition, the normalized return is (almost) guaranteed to fall between
1. Since Procgen environments are designed to have similar difficulties, it’s unlikely that a small subset of environments will dominate this signal. We use the mean normalized return since it offers a better signal than the median, and since we do not need to be robust to outliers.
📁 Competition Structure
The warm-up round evaluates submissions solely on the
CoinRun environment. Participants can become familiar with the codebase and submission pipeline without the need to consider multiple Procgen environments.
Round 1 (General Entry)
Round 1 will evaluate submissions on
3 of the public Procgen environments, as well as on
1 of the private test environments. Participants' final score will be the mean normalized return across these
This round will focus entirely on
sample efficiency, with participants being given a budget of
8M timesteps for training.
Round 2 (Finals)
Round 2 will evaluate submissions on the
16 public Procgen environments, as well as on the
4 private test environments. Participants' final score will be a weighted average of the normalized return across these
20 environments, with the private test environments contributing the same weight as the
16 public environments.
This round will evaluate agents on both sample efficiency and generalization.
Sample efficiency will be measured as before, with agents restricted to training for
Generalization will be measured by restricting agents to
200 levels from each environment during training (as well as
8M total timesteps). In both cases, agents will be evaluated on the full distribution of levels. We will have separate winners for the categories of sample efficiency and generalization.
Because significant computation is required to train and evaluate agents in this final round, only the top 50 submissions from Round 1 will be eligible to submit solutions for Round 2. The leaderboard will report performance on a subset of all environments, specifically on 4 public environments and 1 private test environment.
The top 10 submissions will be subject to a more thorough evaluation, with their performance being averaged over 3 separate training runs. The final winners will be determined by this evaluation.
- June 3rd - July 6th : Warm-Up Round
- July 7th - September 8th : Round 1 (General Entry)
- September 8th - October 19th : Round 2 (Finals)
- October 20th - October 25th : Post Competition Analysis
- October 25th : Final Results Announced
- October 16th - November 10th : Post Competition Wrap-Up
💪 Getting Started
The starter kit of the competition is available at https://github.com/AIcrowd/neurips2020-procgen-starter-kit.
Why evaluate agents using 8M training timesteps?
We evaluate agents using 8M training timesteps since we believe this provides agents enough data to learn reasonable behaviors, while still posing a significant challenge for state of the art algorithms. Our baseline implementation of PPO makes signifcant non-trivial progress over this interval, but it generally fails to converge on most Procgen environments.
When measuring generalization, why restrict agents to 200 levels?
With 200 training levels, we find the generalization gap in many environments is in the golilocks zone -- not too large and not to small (See Figure 13 in the Procgen paper). We believe a training set of this size will provide the best signal to measure algorithmic improvements.
What are the prizes for the competition?
With a generous sponsorship from AWS, we have a Prize Pool consisting of $9000 in cash prizes, and $9000 in AWS Compute Credits.
The organizing team consists of:
- Sharada Mohanty (AIcrowd)
- Karl Cobbe (OpenAI)
- Jyotish Poonganam (AIcrowd)
- Shivam Khandelwal (AIcrowd)
- Christopher Hesse (OpenAI)
- Jacob Hilton (OpenAI)
- John Schulman (OpenAI)
- William H. Guss (OpenAI)
If you have any questions, please contact Sharada Mohanty (email@example.com) or Karl Cobbe (firstname.lastname@example.org).
newton7777Over 1 year ago