In this repository some of my experiments with Reinforcement Learning algorithms based on OpenAi Gym ToolKit
Packages:
- openai/envs the OpenAi Gym compatible environments for evaluation
- openai/agents the learning agents
Environments:
- NArmedBanditEnv - N-armed bandit (stationary, nonstationary)
Learning agents:
- SampleAverageActionValueAgent - the learning agent based on sample-average action-value selection algorithm for both stationary and nonstationary environments
import gym
from openai.agents.sampleaverage import SampleAverageActionValueAgent
def main():
# load environment
env = gym.make('10ArmedBanditStationary-v0')
# setup
agent = SampleAverageActionValueAgent(num_actions = 10)
episode_count = 1
max_steps = 100
reward = 0
done = False
for i in xrange(episode_count):
ob = env.reset()
for j in xrange(max_steps):
action = agent.evaluate(reward, done)
ob, reward, done, _ = env.step(action)
if done:
break
if __name__ == '__main__':
main()
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