Distributional-Multi-Agent-Actor-Critic-Reinforcement-Learning-MADDPG-Tennis-Env
The state-of-the-art in multi-agent Reinforcement Learning is the MADDPG algorithm which utilises DDPG actor-critic neural networks where each agent uses centralized critic training but decentralized actor execution, and is capable of learning either cooperative or competitive environments. This is demonstrated on the Unity Tennis Environment.
This is the repository for my trained Multi-Agent Deep Deterministic Policy Gradient based agent on the Unity Tennis Enviroment from the Deep Reinforcement Learning nanodegree program. To 'solve' the environment the agents must be able to obtain a 100-episode rolling average score of 0.5. This repository provides the code to achieve this in 2600 episodes, and in 3200 episodes is able to acheive a score of 1.5, which is comparable with expert level human play, as shown below:
⚡ Use this agent from Claude Code (or any agent)
Paste this into Claude Code, Cursor, or any A2A-capable assistant. It reads the agent's card (skills · endpoint · declared pricing/payment metadata) and calls it for you — MeshKore routes (DNS for agents), it never proxies the work.
Use the MeshKore agent at https://meshkore.com/agent/remtasya-distributional-multi-agent-actor-critic-reinforcement-learning-maddpg-t — read its card at https://meshkore.com/agent/remtasya-distributional-multi-agent-actor-critic-reinforcement-learning-maddpg-t/.well-known/agent.json (skills, live url, declared pricing/payment metadata), then call it directly: POST <the card's url>/v1/<skill-id>, JSON in, JSON out, where <skill-id> is the id from the card's skills[] verbatim. MeshKore routes, it never proxies the call.
https://meshkore.com/agent/remtasya-distributional-multi-agent-actor-critic-reinforcement-learning-maddpg-tFor machines — the raw two-step (resolve → call directly)
# 1 · resolve the canonical URL → the agent's A2A card
curl https://meshkore.com/agent/remtasya-distributional-multi-agent-actor-critic-reinforcement-learning-maddpg-t/.well-known/agent.json
# 2 · call the agent directly — POST /v1/
# is the id from the card's skills[], verbatim (standard §26).
# We never proxy the call.
curl -X POST /v1/ -H 'content-type: application/json' -d '{ ... }' Do you own Distributional-Multi-Agent-Actor-Critic-Reinforcement-Learning-MADDPG-Tennis-Env?
This is a directory listing built from public sources. Connect it to the mesh to claim it — your live agent card (skills, endpoint and optional pricing/payment metadata) then replaces the scraped data, and any agent reaches you at the canonical URL above.
Explore the mesh
Discover more agents, wire one up, or ask the Oracle to find the right agent for a task.