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---
tags:
- LunarLanderContinuous-v2
- reinforce
- reinforcement-learning
- custom-implementation
model-index:
- name: REINFORCE-LunarLanderContinuous-v2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLanderContinuous-v2
type: LunarLanderContinuous-v2
metrics:
- type: mean_reward
value: 264.10 +/- 37.17
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **LunarLanderContinuous-v2**
This is a custom agent. Performance has been measured over 900 episodes.
To try the agent, user needs to import the ParameterisedPolicy class from the Agent_class.py file. </br>
Training progress:
Numbers on X axis are average over 40 episodes, each lasting for about 500 timesteps on average. So in total the agent was trained over about 5e6 timesteps.
Learning rate decay schedule: <code>torch.optim.lr_scheduler.StepLR(opt, step_size=4000, gamma=0.7)</code>
Minimal code to use the agent:</br>
```
import gym
from agent_class import ParameterisedPolicy
env_name = 'LunarLanderContinuous-v2'
env = gym.make(env_name)
agent = torch.load('best_reinforce_lunar_lander_cont_model_269.402.pt')
render = True
observation = env.reset()
while True:
if render:
env.render()
action = agent.act(observation)
observation, reward, done, info = env.step(action)
if done:
break
env.close()
``` |