MPE (Multi-Agent Particle Environments)
E438350
MPE (Multi-Agent Particle Environments) is a classic collection of lightweight 2D multi-agent reinforcement learning benchmark environments featuring simple particle-based agents and tasks like cooperation, competition, and communication.
All labels observed (1)
| Label | Occurrences |
|---|---|
| MPE (Multi-Agent Particle Environments) canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T4425216 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: MPE (Multi-Agent Particle Environments) Context triple: [PettingZoo, hasEnvironmentType, MPE (Multi-Agent Particle Environments)]
-
A.
Arcade Learning Environment
Arcade Learning Environment is a widely used research platform that provides a suite of Atari 2600 games for developing and evaluating reinforcement learning algorithms.
-
B.
OpenAI Baselines
OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
-
C.
MuJoCo environments
MuJoCo environments are physics-based continuous control simulation tasks widely used in reinforcement learning research and benchmarking.
-
D.
Dueling DQN
Dueling DQN is a deep reinforcement learning algorithm that separates state-value and advantage estimations within its neural network architecture to improve learning efficiency and stability over standard DQN.
-
E.
DDPG
DDPG (Deep Deterministic Policy Gradient) is a model-free, off-policy deep reinforcement learning algorithm designed for continuous action spaces, combining ideas from DQN and actor-critic methods.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: MPE (Multi-Agent Particle Environments) Target entity description: MPE (Multi-Agent Particle Environments) is a classic collection of lightweight 2D multi-agent reinforcement learning benchmark environments featuring simple particle-based agents and tasks like cooperation, competition, and communication.
-
A.
Arcade Learning Environment
Arcade Learning Environment is a widely used research platform that provides a suite of Atari 2600 games for developing and evaluating reinforcement learning algorithms.
-
B.
OpenAI Baselines
OpenAI Baselines is a collection of high-quality reference implementations of reinforcement learning algorithms released by OpenAI for research and benchmarking.
-
C.
MuJoCo environments
MuJoCo environments are physics-based continuous control simulation tasks widely used in reinforcement learning research and benchmarking.
-
D.
Dueling DQN
Dueling DQN is a deep reinforcement learning algorithm that separates state-value and advantage estimations within its neural network architecture to improve learning efficiency and stability over standard DQN.
-
E.
DDPG
DDPG (Deep Deterministic Policy Gradient) is a model-free, off-policy deep reinforcement learning algorithm designed for continuous action spaces, combining ideas from DQN and actor-critic methods.
- F. None of above. chosen
Statements (50)
| Predicate | Object |
|---|---|
| instanceOf |
multi-agent reinforcement learning benchmark suite
ⓘ
research tool ⓘ software environment collection ⓘ |
| acronym | MPE NERFINISHED ⓘ |
| agentActionSpace | continuous control actions ⓘ |
| agentType | simple point-mass particles ⓘ |
| associatedAlgorithm | MADDPG NERFINISHED ⓘ |
| complexityLevel | low-dimensional ⓘ |
| designGoal |
simplicity for rapid experimentation
ⓘ
standardized multi-agent RL benchmarks ⓘ |
| domain |
multi-agent reinforcement learning
ⓘ
reinforcement learning ⓘ |
| environmentType | discrete-time simulation ⓘ |
| feature |
continuous 2D space
ⓘ
lightweight 2D environments ⓘ particle-based agents ⓘ |
| fullName | Multi-Agent Particle Environments NERFINISHED ⓘ |
| hasStateElements |
agent positions
ⓘ
agent velocities ⓘ landmark positions ⓘ |
| implementationLanguage | Python NERFINISHED ⓘ |
| includesEnvironment |
simple_adversary
ⓘ
simple_crypto ⓘ simple_push ⓘ simple_reference ⓘ simple_speaker_listener ⓘ simple_spread ⓘ simple_spread_comm ⓘ simple_tag ⓘ simple_world_comm ⓘ |
| license | MIT License ⓘ |
| observationType | partial observations ⓘ |
| originallyReleasedWith | paper "Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments" NERFINISHED ⓘ |
| rewardStructure |
competitive rewards
ⓘ
cooperative rewards ⓘ mixed rewards ⓘ |
| supportsAgentHeterogeneity | true ⓘ |
| supportsCommunicationChannels | true ⓘ |
| supportsFramework | OpenAI Gym-style interface ⓘ |
| supportsTaskType |
communication
ⓘ
competition ⓘ cooperation ⓘ mixed cooperative-competitive tasks ⓘ |
| typicalNumberOfAgents | multiple agents per environment ⓘ |
| typicalUseContext | academic research ⓘ |
| usedFor |
benchmarking multi-agent RL algorithms
ⓘ
evaluating centralized training with decentralized execution ⓘ evaluating decentralized policies ⓘ studying coordination between agents ⓘ studying emergent communication ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: MPE (Multi-Agent Particle Environments) Description of subject: MPE (Multi-Agent Particle Environments) is a classic collection of lightweight 2D multi-agent reinforcement learning benchmark environments featuring simple particle-based agents and tasks like cooperation, competition, and communication.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.