We lifted 6 functions out of this paper's own repositories and ran 6 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| chandar-lab/adaptive-hanabi | canonical | 6 of 6 |
| Function | Status | Where it lives |
|---|---|---|
| color_idx_to_char | Ran | chandar-lab/adaptive-hanabi/hanabi-learning-environment/pyhanabi.py code served (permissive licence) · get_code("e8b86475e9cc43ba") |
| invalid_range | Ran | chandar-lab/adaptive-hanabi/hanabi-learning-environment/agents/rainbow/replay_memory.py code served (permissive licence) · get_code("2ddf38ce2ee629f1") |
| linearly_decaying_epsilon | Ran | chandar-lab/adaptive-hanabi/hanabi-learning-environment/agents/rainbow/dqn_agent.py code served (permissive licence) · get_code("befc653f7130aea6") |
| project_distribution | Ran | chandar-lab/adaptive-hanabi/hanabi-learning-environment/agents/rainbow/rainbow_agent.py code served (permissive licence) · get_code("e7b34216d440cf4a") |
| try_cdef | Ran | chandar-lab/adaptive-hanabi/hanabi-learning-environment/pyhanabi.py code served (permissive licence) · get_code("66eb94ec1e1467ae") |
| try_load | Ran | chandar-lab/adaptive-hanabi/hanabi-learning-environment/pyhanabi.py code served (permissive licence) · get_code("6ea5a7ed1e5d87a8") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Cooperative Multi-agent Reinforcement Learning (MARL) algorithms with Zero-Shot Coordination (ZSC) have gained significant attention in recent years. ZSC refers to the ability of agents to coordinate zero-shot (without additional interaction experience) with independently trained agents. While ZSC is crucial for cooperative MARL agents, it might not be possible for complex tasks and changing environments. Agents also need to adapt and improve their performance with minimal interaction with other agents. In this work, we show empirically that state-of-the-art ZSC algorithms have poor performance when paired with agents trained with different learning methods, and they require millions of interaction samples to adapt to these new partners. To investigate this issue, we formally defined a framework based on a popular cooperative multi-agent game called Hanabi to evaluate the adaptability of MARL methods. In particular, we created a diverse set of pre-trained agents and defined a new metric called adaptation regret that measures the agent's ability to efficiently adapt and improve its coordination performance when paired with some held-out pool of partners on top of its ZSC performance. After evaluating several SOTA algorithms using our framework, our experiments reveal that naive Independent Q-Learning (IQL) agents in most cases adapt as quickly as the SOTA ZSC algorithm Off-Belief Learning (OBL). This finding raises an interesting research question: How to design MARL algorithms with high ZSC performance and capability of fast adaptation to unseen partners. As a first step, we studied the role of different hyper-parameters and design choices on the adaptability of current MARL algorithms. Our experiments show that two categories of hyper-parameters controlling the training data diversity and optimization process have a significant impact on the adaptability of Hanabi agents.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2308.10284")
get_code_for_paper("2308.10284")
have("2308.10284")
Connect an agent — have() is free.