Visrl (pronounced “visceral”) is a simple wrapper to analyse and visualise reinforcement learning agents’ behaviour in the environment.
Reinforcement learning requires a lot of overhead code to inspect an agent’s behaviour visually, typically through
env.render(). Visrl allows users to easily intervene and switch between agent control and human control, and allows inserting a breakpoint in the game state to pause only at a relevant state of interest.
- Set action hotkeys
- Human intervention: Take actions 1 step at a time
- Agent control: Return control to the agent
- Speed up/ slow down frame rate
- Visualise relevant values across history
- Breakpoint: Run until a condition involving values is fulfilled
- Playback: Show past frames and ations
- Record: Record a .mp4, .gif or download a .csv of the history.
pip install visrl
import gym from stable_baselines3 import DQN from visrl import Visrl env = gym.make('LunarLander-v2') agent = DQN('MlpPolicy', env, verbose=1) agent.learn(total_timesteps=int(2e5)) Visrl(env, agent).run()