Description
This GitHub repository, "handful-of-trials," hosts the experimental code for the research paper "Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics Models." The project focuses on model-based reinforcement learning (RL) and addresses the common challenge where model-based algorithms, despite their sample efficiency, often underperform model-free algorithms in asymptotic performance, particularly when using high-capacity function approximators like deep networks.
Read More
The core innovation presented is the Probabilistic Ensembles with Trajectory Sampling (PETS) algorithm. PETS bridges the performance gap by employing uncertainty-aware dynamics models. It integrates deep network dynamics models that are aware of their own uncertainties with sophisticated sampling-based uncertainty propagation techniques. This approach allows the model to learn effectively from limited data, a critical factor in many real-world RL applications where data collection is expensive or time-consuming.
The repository provides the necessary code to reproduce the experiments described in the paper. It includes scripts for running experiments on various benchmark tasks, such as cartpole, reacher, pusher, and halfcheetah. Users can configure experiments by specifying environment names, controller arguments (e.g., model type, propagation method, optimizer), and parameter overrides. The code also includes utilities for visualizing rollouts and analyzing results, with trial data logged in a structured format.
To facilitate ease of use, the repository offers a Dockerfile for creating a self-contained environment, including necessary dependencies like MuJoCo. A pre-built Docker image is also available. The project is written primarily in Python, with a significant portion of the code dedicated to the implementation of the PETS algorithm and its associated components, such as probabilistic ensembles and trajectory sampling methods. The research aims to demonstrate that model-based RL can achieve state-of-the-art asymptotic performance while requiring substantially fewer samples than leading model-free methods.
- CategoryAI Models & LLMs
- Pricing ModelOpen source
- DeploymentCloud, Self-hosted
- PlatformsLinux
Handful of Trials's Core Features
Implementation of the PETS algorithm for deep reinforcement learning.
Utilizes uncertainty-aware deep network dynamics models.
Employs sampling-based uncertainty propagation for efficient learning.
Code for reproducing experiments on benchmark RL tasks.
Includes scripts for running and configuring experiments.
Provides visualization tools for rollouts.
Offers a Dockerfile and pre-built image for environment setup.
Supports various controller configurations including model type, propagation, and optimization methods.
Getting Started with Handful of Trials
Clone the repository: Obtain the code from the GitHub repository.
Set up environment: Install dependencies using `requirements.txt` or use the provided Dockerfile/image.
Configure experiments: Specify environment, controller arguments, and overrides as needed.
Run experiments: Execute experiments using `python scripts/mbexp.py` with desired parameters.
Analyze results: Examine logged data in `logs.mat` for observations, actions, rewards, and returns.
Visualize rollouts: Use `python scripts/render.py` to visualize trained model performance.
Handful of Trials's Use Cases
- Sample Efficient RL
- Model-Based RL Research
- Robotics Control
- Benchmark RL Tasks
- Uncertainty Quantification in RL
- Deep Learning for Dynamics Modeling
