RLLama
Empowering LLMs with Memory-Augmented Reinforcement Learning
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Description
RLlama is an enhanced fork of LlamaGym that supercharges it with memory-augmented learning capabilities and additional reinforcement learning (RL) algorithms. It introduces episodic memory and working memory, allowing agents to learn from past experiences and maintain context for current decision-making, making it a powerful tool for developing intelligent agents in various environments.
How to use RLLama?
To get started with RLlama, simply install it using pip with the command 'pip install rllama'. You can then create agents for various environments, such as Blackjack or text-based games, by importing the RLlamaAgent class and defining the necessary methods for your agent's behavior.
Core features of RLLama:
1️⃣
Memory-Augmented Learning with Episodic and Working Memory
2️⃣
Multiple RL Algorithms (PPO, DQN, A2C, SAC, REINFORCE, GRPO)
3️⃣
Online Learning Support
4️⃣
Seamless Integration with Gymnasium
5️⃣
Multi-Modal Support (Coming Soon)
Why could be used RLLama?
| # | Use case | Status | |
|---|---|---|---|
| # 1 | Developing intelligent agents for card games like Blackjack | ✅ | |
| # 2 | Creating agents for text-based adventure games | ✅ | |
| # 3 | Implementing memory-augmented learning in various RL environments | ✅ | |
Who developed RLLama?
RLlama is developed by Ch33nchan, who has contributed to the field of reinforcement learning and memory-augmented learning, enhancing the capabilities of LlamaGym to create a more powerful framework for building intelligent agents.