This is a helper package designed to help you start the AI for Prosthetics challenge.
The package contains run.py that can train, test or submit any agent in /helper/baselines or /agent directory. For example, to train TensorforcePPOAgent for 1000 steps, run
./run.py TensorforcePPOAgent --train 1000After you trained the agent sufficiently, test the agent locally.
./run.py TensorforcePPOAgentYou can also test the agent with visualization with the -v/--visualize flag.
./run.py TensorforcePPOAgent -vIf you are satisfied with the result, you can submit the agent to CrowdAI with the -s/--submit flag.
./run.py RandomAgent -sNote that you need to first add your API token to CONFIG.py to submit any agents. Also note that you can only submit 5 times each 24 hours (Issue #141).
To understand the environment, consider running the most basic agents. There are two non-learning baseline agents: RandomAgent and FixedActionAgent. The RandomAgent chooses a random action at every timestep, and the FixedActionAgent chooses the same action at every timestep. Try running the agent locally to gain some intuition about the environment and the competition.
The KerasDDPGAgent uses the Deep Deterministic Policy Gradient algorithm by Lillicrap et al. (2015). To use this agent, you need the keras-rl package.
The TensorforcePPOAgent uses the Proximal Policy Optimization algorithm by Schulman et al. (2017). To use this agent, you need the tensorforce package.
You can add custom agents to the /agent directory. The directory contains DoNothingAgent to serve as an example for custom agents. All agents in the /agents directory is imported in ./run.py, so you can use the same commands as above. If you would like to change the network architecture or hyperparameters of the keras-rl or tensorforce agents, you can also copy the baseline agent class to this directory and modify it.
from helper.templates import Agent
class DoNothingAgent(Agent):
"""
An agent that chooses NOOP action at every timestep.
"""
def __init__(self, observation_space, action_space):
self.action = [0] * action_space.shape[0]
def act(self, observation):
return self.actionI am writing a post every week about the competition in my blog.
Of course, you should always check the official page for updates.
If you have any questions about the competition, ask them in Gitter or CrowdAI discussion page!
Go to this link with [username] replaced with your actual CrowdAI username:
https://www.crowdai.org/participants/[username]
You should see the text API key: XXXX. That is your token!
According to Issue #141, you are limited to 5 submissions in a 24-hour window. It seems like the counter is incremented in the beginning when you call client.env_create(), so make sure your code is working before attempting to submit!