MAgent
RL platform
A platform for multi-agent reinforcement learning research and development
A Platform for Many-Agent Reinforcement Learning
2k stars
68 watching
334 forks
Language: Python
last commit: over 2 years ago
Linked from 2 awesome lists
deep-learningmulti-agentreinforcement-learning
Related projects:
Repository | Description | Stars |
---|---|---|
| A Python library providing reinforcement learning building blocks for implementing agent policies and functions | 1,272 |
| A reinforcement learning library for Go, providing a set of agents to solve challenges in various environments. | 345 |
| Assesses generalization of multi-agent reinforcement learning algorithms to novel social situations | 637 |
| A collection of implementations of Reinforcement Learning and planning algorithms in Python. | 596 |
| A PyTorch framework for accelerating reinforcement learning research and development by providing a modular, reusable, and customizable training loop | 46 |
| Trains an RL agent to execute natural language instructions in a 3D environment using a combination of A3C and gated attention mechanisms. | 237 |
| A benchmark suite for unsupervised reinforcement learning agents, providing pre-trained models and scripts for testing and fine-tuning agent performance. | 335 |
| Replication of Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks in PyTorch for reinforcement learning tasks | 830 |
| Provides benchmarking policies and datasets for offline reinforcement learning | 85 |
| An implementation of an actor-critic reinforcement learning algorithm in Python. | 245 |
| A collection of reinforcement learning algorithms and tools for training agents in complex environments. | 43 |
| A high-throughput reinforcement learning library with optimized synchronous and asynchronous implementations of policy gradients. | 839 |
| A framework for parallel population-based reinforcement learning | 507 |
| An RL framework for building and training reinforcement learning models in Python | 266 |
| A framework for implementing complex reinforcement learning algorithms with flexibility and ease of implementation | 306 |