Package: kaggle-environments
Environmentspipinstallkaggle-environmentsTLDR;fromkaggle_environmentsimportmake# Setup a tictactoe environment.env=make("tictactoe")# Basic agent which marks the first available cell.defmy_agent(obs):return[cforcinrange(len(obs.board))ifobs.board[c]==0][0]# Run the basic agent against a default agent which chooses a "random" move.env.run([my_agent,"random"])# Render an html ipython replay of the tictactoe game.env.render(mode="ipython")OverviewKaggle Environments was created to evaluate episodes. While other libraries have set interface precedents (such as Open.ai Gym), the emphasis of this library focuses on:Episode evaluation (compared to training agents).Configurable environment/agent lifecycles.Simplified agent and environment creation.Cross language compatible/transpilable syntax/interfaces.Help Documentation# Additional documentation (especially interfaces) can be found on all public functions:fromkaggle_environmentsimportmakehelp(make)env=make("tictactoe")dir(env)help(env.reset)AgentsA function which given an observation generates an action.WritingAgent functions can have observation and configuration parameters and must return a valid action. Details about the observation, configuration, and actions can seen by viewing the specification.fromkaggle_environmentsimportmakeenv=make("connectx",{"rows":10,"columns":8,"inarow":5})defagent(observation,configuration):print(observation)# {board: [...], mark: 1}print(configuration)# {rows: 10, columns: 8, inarow: 5}return3# Action: always place a mark in the 3rd column.# Run an episode using the agent above vs the default random agent.env.run([agent,"random"])# Print schemas from the specification.print(env.specification.observation)print(env.specification.configuration)print(env.specification.action)Loading AgentsAgents are always functions, however there are some shorthand syntax options to make generating/using them easier.# Agent def accepting an observation and returning an action.defagent1(obs):return[cforcinrange(len(obs.board))ifobs.board[c]==0][0]# Load a default agent called "random".agent2="random"# Load an agent from source.agent3="""def act(obs):return [c for c in range(len(obs.board)) if obs.board[c] == 0][0]"""# Load an agent from a file.agent4="C:\path\file.py"# Return a fixed action.agent5=3# Return an action from a url.agent6="http://localhost:8000/run/agent"Default AgentsMost environments contain default agents to play against. To see the list of available agents for a specific environment run:fromkaggle_environmentsimportmakeenv=make("tictactoe")# The list of available default agents.print(*env.agents)# Run random agent vs reaction agent.env.run(["random","reaction"])TrainingOpen AI Gym interface is used to assist with training agents. TheNonekeyword is used below to denote which agent to train (i.e. train as first or second player of connectx).fromkaggle_environmentsimportmakeenv=make("connectx",debug=True)# Training agent in first position (player 1) against the default random agent.trainer=env.train([None,"random"])obs=trainer.reset()for_inrange(100):env.render()action=0# Action for the agent being trained.obs,reward,done,info=trainer.step(action)ifdone:obs=trainer.reset()DebuggingThere are 3 types of errors which can occur from agent execution:Timeout- the agent runtime exceeded the allowed limit. There are 2 timeouts:agentTimeout- Used for initialization of an agent on first "act".actTimeout- Used for obtaining an action.Error- the agent raised and error during execution.Invalid- the agent action response didn't match the action specification or the environment deemed it invalid (i.e. playing twice in the same cell in tictactoe).To help debug your agent and why it threw the errors above, add thedebugflag when setting up the environment.fromkaggle_environmentsimportmakedefagent():return"Something Bad"env=make("tictactoe",debug=True)env.run([agent,"random"])# Prints: "Invalid Action: Something Bad"EnvironmentsA function which given a state and agent actions generates a new state.NameDescriptionMakeconnectxConnect 4 in a row but configurable.env = make("connectx")tictactoeClassic Tic Tac Toeenv = make("tictactoe")identityFor debugging, action is the reward.env = make("identity")MakingAn environment instance can be made from an existing specification (such as those listed above).fromkaggle_environmentsimportmake# Create an environment instance.env=make(# Specification or name to registered specification."connectx",# Override default and environment configuration.configuration={"rows":9,"columns":10},# Initialize the environment from a prior state (episode resume).steps=[],# Enable verbose logging.debug=True)ConfigurationThere are two types of configuration: Defaults applying to every environment and those specific to the environment. The following is a list of the default configuration:NameDescriptionepisodeStepsMaximum number of steps in the episode.agentTimeoutMaximum runtime (seconds) to initialize an agent.actTimeoutMaximum runtime (seconds) to obtain an action from an agent.runTimeoutMaximum runtime (seconds) of an episode (not necessarily DONE).maxLogLengthMaximum log length (number of characters,None-> no limit)env=make("connectx",configuration={"columns":19,# Specific to ConnectX."actTimeout":10,})ResettingEnvironments are reset by default after "make" (unless starting steps are passed in) as well as when calling "run". Reset can be called at anytime to clear the environment.num_agents=2reset_state=env.reset(num_agents)RunningExecute an episode against the environment using the passed in agents until they are no longer running (i.e. status != ACTIVE).steps=env.run([agent1,agent2])print(steps)EvaluatingEvaluation is used to run an episode (environment + agents) multiple times and just return the rewards.fromkaggle_environmentsimportevaluate# Same definitions as "make" above.environment="connectx"configuration={"rows":10,"columns":8,"inarow":5}steps=[]# Which agents to run repeatedly.  Same as env.run(agents)agents=["random",agent1]# How many times to run them.num_episodes=10rewards=evaluate(environment,agents,configuration,steps,num_episodes)SteppingRunning above essentially just steps until no agent is still active. To execute a singular game loop, pass in actions directly for each agent. Note that this is normally used for training agents (most useful in a single agent setup such as using the gym interface).agent1_action=agent1(env.state[0].observation)agent2_action=agent2(env.state[1].observation)state=env.step([agent1_action,agent2_action])PlayingA few environments offer an interactive play against agents within jupyter notebooks. An example of this is using connectx:fromkaggle_environmentsimportmakeenv=make("connectx")# None indicates which agent will be manually played.env.play([None,"random"])RenderingThe following rendering modes are supported:json - Same as doing a json dump ofenv.toJSON()ansi - Ascii character representation of the environment.human - ansi just printed to stdouthtml - HTML player representation of the environment.ipython - html just printed to the output of a ipython notebook.out=env.render(mode="ansi")print(out)Command Line>pythonmain.py-hList Registered Environments>pythonmain.pylistEvaluate Episode Rewardspythonmain.pyevaluate--environmenttictactoe--agentsrandomrandom--episodes10Run an Episode>pythonmain.pyrun--environmenttictactoe--agentsrandom/pathtomy/agent.py--debugTrueLoad an EpisodeThis is useful when converting an episode json output into html.pythonmain.pyload--environmenttictactoe--steps[...]--render'{"mode": "html"}'HTTP ServerThe HTTP server contains the same interface/actions as the CLI above merging both POST body and GET params.Setuppythonmain.pyhttp-server--port=8012--host=0.0.0.0Running Agents on Separate Servers# How to run agent on a separate server.importrequestsimportjsonpath_to_agent1="/home/ajeffries/git/playground/agent1.py"path_to_agent2="/home/ajeffries/git/playground/agent2.py"agent1_url=f"http://localhost:5001?agents[]={path_to_agent1}"agent2_url=f"http://localhost:5002?agents[]={path_to_agent2}"body={"action":"run","environment":"tictactoe","agents":[agent1_url,agent2_url]}resp=requests.post(url="http://localhost:5000",data=json.dumps(body)).json()# Inflate the response replay to visualize.fromkaggle_environmentsimportmakeenv=make("tictactoe",steps=resp["steps"],debug=True)env.render(mode="ipython")print(resp)