How to evaluate RL agents performance

In GenerativeRL, the performance of reinforcement learning (RL) agents is evaluated using simulators or environments.

The class of agent is implemented as a class under the grl.agents module, which has a unified act method that takes the observation as input and returns the action.

User can evaluate the performance of an agent by running it in a simulator or environment and collecting the rewards.

import gym
agent = algorithm.deploy()
env = gym.make(config.deploy.env.env_id)
observation = env.reset()
for _ in range(config.deploy.num_deploy_steps):
    env.render()
    observation, reward, done, _ = env.step(agent.act(observation))