# Docs Fetchai Agents Aea > The AEA gym skill demonstrates how a custom Reinforcement Learning agent, that uses OpenAI's gym library, may be embedded into an AEA skill and connection. - Skill: `majiayu000/docs-fetchai-agents-aea` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds add majiayu000/docs-fetchai-agents-aea` - Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/docs-fetchai-agents-aea/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - Author: majiayu000 (https://skillmd.com/u/majiayu000) - Updated: 2026-09-09 - Page: https://skillmd.com/skills/majiayu000/docs-fetchai-agents-aea --- # Gym Skill The AEA gym skill demonstrates how a custom Reinforcement Learning agent, that uses OpenAI's gym library, may be embedded into an AEA skill and connection. ## Discussion The gym skills demonstrate how to wrap a Reinforcement Learning agent in a skill. The example decouples the RL agent from the `gym.Env` allowing them to run in separate execution environments, potentially owned by different entities. ## Preparation Instructions ### Dependencies Follow the Preliminaries and Installation sections from the AEA quick start. Download the necessary directories into your working directory: ``` bash mkdir gym_skill_agent svn export https://github.com/fetchai/agents-aea.git/trunk/examples ``` Install the `gym` and `numpy` library. ``` bash pip install numpy gym ``` ## Demo Instructions ### Create the AEA First, fetch the gym AEA: ``` bash aea fetch fetchai/gym_aea:0.26.5 --alias my_gym_aea cd my_gym_aea aea install ``` ??? note "Alternatively, create from scratch:" ### Create the AEA In the root directory, create the gym AEA and enter the project. ``` bash aea create my_gym_aea cd my_gym_aea ``` ### Add the gym skill ``` bash aea add skill fetchai/gym:0.21.6 ``` ### Set gym connection as default ``` bash aea config set agent.default_connection fetchai/gym:0.20.6 ``` ### Install the skill dependencies To install the `gym` package, a dependency of the gym skill, from PyPI run ``` bash aea install ``` ### Set up the Training Environment #### Copy the Gym Environment to the AEA Directory ``` bash mkdir gyms cp -a ../examples/gym_ex/gyms/. gyms/ ``` #### Update the Connection Configuration ``` bash aea config set vendor.fetchai.connections.gym.config.env 'gyms.env.BanditNArmedRandom' ``` #### Create and Add a Private Key ``` bash aea generate-key fetchai aea add-key fetchai ``` ### Run the AEA with the Gym Connection ``` bash aea run ``` You will see the gym training logs. AEA gym training logs ### Delete the AEA When you're done, you can go up a level and delete the AEA. ``` bash cd .. aea delete my_gym_aea ``` ## Communication This diagram shows the communication between the AEA and the gym environment ``` mermaid sequenceDiagram participant AEA participant Environment activate AEA activate Environment AEA->>Environment: reset loop learn AEA->>Environment: act Environment->>AEA: percept end AEA->>Environment: close deactivate AEA deactivate Environment ``` ## Skill Architecture The skill consists of two core components: `GymHandler` and `GymTask`. In the `setup` method of the `GymHandler` the `GymTask` is initialized, as well as its `setup` and `execute` methods called. The handler, which is registered against the `GymMessage.protocol_id` then filters for messages of that protocol with the performative `GymMessage.Performative.PERCEPT`. These messages are passed to the `proxy_env_queue` of the task. The `GymTask` is responsible for training the RL agent. In particular, `MyRLAgent` is initialized and trained against `ProxyEnv`. The `ProxyEnv` instantiates a `gym.Env` class and therefore implements its API. This means the proxy environment is compatible with any `gym` compatible RL agent. However, unlike other environments it only acts as a proxy and does not implement an environment of its own. It allows for the decoupling of the process environment of the `gym.env` from the process environment of the RL agent. The actual `gym.env` against which the agent is trained is wrapped by the `gym` connection. The proxy environment and gym connection communicate via a protocol, the `gym` protocol. Note, it would trivially be possible to implement the `gym` environment in another AEA; this way one AEA could provide `gym` environments as a service. Naturally, the overhead created by the introduction of the extra layers causes a higher latency when training the RL agent. In this particular skill, which chiefly serves for demonstration purposes, we implement a very basic RL agent. The agent trains a model of price of `n` goods: it aims to discover the most likely price of each good. To this end, the agent randomly selects one of the `n` goods on each training step and then chooses as an `action` the price which it deems is most likely accepted. Each good is represented by an id and the possible price range `[1,100]` divided into 100 integer bins. For each price bin, a `PriceBandit` is created which models the likelihood of this price. In particular, a price bandit maintains a beta distribution. The beta distribution is initialized to the uniform distribution. Each time the price associated with a given `PriceBandit` is accepted or rejected the distribution maintained by the `PriceBandit` is updated. For each good, the agent can therefore over time learn which price is most likely. Gym skill illustration The illustration shows how the RL agent only interacts with the proxy environment by sending it `action (A)` and receiving `observation (O)`, `reward (R)`, `done (D)` and `info (I)`.