openai/gymArchived
Gym
Gym is a reinforcement learning environment toolkit and agent simulation framework. It provides a standardized API and a universal communication interface that defines how learning agents interact with simulation environments through actions and observations.
The project includes a benchmark environment suite and a diverse library of pre-configured simulation worlds, including physics engines and classic control tasks. It enables the creation of custom simulation environments to train agents in specific operational scenarios while ensuring reproducibility across different learning algorithms.
The framework manages state-transition simulations, mapping raw observation data and translating agent decisions into compatible action values. It utilizes environment wrappers to modify observations or rewards and a versioned registry to maintain consistency across benchmarks.
Features
- Reinforcement Learning Environments - Provides a comprehensive toolkit of simulation environments for training and refining reinforcement learning agents.
- Agent Simulation Environments - Executes agent steps within virtual environments by processing actions and returning observations and rewards.
- Custom Environment Definitions - Enables building virtual worlds with a standardized interface to train agents in specific or unique operational scenarios.
- Benchmarking Suites - Ships a set of curated physics engines and control tasks used to ensure reproducible results across learning algorithms.
- Reinforcement Learning Simulators - Provides a system for executing steps within virtual worlds to evaluate the performance of reinforcement learning policies.
- Performance Benchmarking - Allows for the comparative analysis of agent performance across a consistent set of environments and rules.
- Reinforcement Learning Environments - Provides a standardized toolkit of simulation spaces and interfaces for developing and benchmarking RL agents.
- RL Interface Standards - Provides a universal set of communication rules and APIs ensuring consistency when developing and comparing different reinforcement learning algorithms.
- State-Transition Execution - Processes agent actions to compute the next environment state, associated reward, and termination status.
- Standardized Interfaces - Defines a universal set of method signatures for interactions between learning algorithms and simulation environments.
- Environment Registries - Maps specific version identifiers to environment creators to ensure consistent benchmarks across different software releases.
- RL Reproducibility Standards - Ensures consistent results across different trials by using versioned and standardized simulation environments.
- Simulation Environment Libraries - Includes a diverse library of pre-configured simulation worlds, including physics engines and classic control tasks.
- Environment Version Locking - Locks environment versions to prevent changes in simulation logic from invalidating comparative results between algorithms.
- Observation Standardizers - Maps raw simulation data into standardized formats like tensors or discrete integers for agent processing.
- Simulator Wrappers - Provides standardized interfaces for wrapping simulation logic to modify observations or rewards without altering the core engine.
- Machine Learning Frameworks - Toolkit for developing and benchmarking reinforcement learning agents.
- Model-Based Reinforcement Learning - Standardized toolkit for developing and comparing reinforcement learning algorithms.
- Perception and Machine Learning - Toolkit for developing reinforcement learning algorithms.
- Reinforcement Learning Environments - Standard toolkit for developing and comparing reinforcement learning algorithms.
- Research Platforms - Standard toolkit for developing and comparing reinforcement learning algorithms.
- World Models - Toolkit for developing and comparing reinforcement learning algorithms.
- Development Resources - Standardizes environments for reinforcement learning research.
