Comments (2)
instead of line 161 action = dqn.act(state)
you can put an if statement and do
action = env.action_space.sample()
for whatever condition you want. But not sure why you would want to do this since rainbow has roughly random actions at the start of training.
If rainbow keeps doing the same action you should tune your noisy layer parameters.
from cups-rl.
Thank you. It is because initially my idea is to reset the agent's position in the room once certain conditions are met. However, since the currently wrapper does not support teleport, I compromised my strategy. Instead of returning to the original position, I make the agent take a random walk so that it has no way of tracing back to its position when the conditions are met in an analytic way. But if you know an alternative of resetting the agent's position do let me know. Thanks!
from cups-rl.
Related Issues (18)
- Mulit agent HOT 10
- How to add in new movement like Crouch and stand HOT 1
- Change the maximum number of steps for one eposide HOT 1
- How to train on server? HOT 2
- Is there any ways to improve trainning speed? HOT 5
- Calling different tasks for different iteration HOT 2
- ploting the result HOT 2
- Regarding to policy/model/weights HOT 4
- error after num_steps = 100000 / 50000000 HOT 2
- reset agent but not the envirnment HOT 3
- "open_close_interaction": true gives error HOT 1
- Randomize object's location
- decouple RL algorithm with gym wrapper
- regarding the input of Rainbow
- Error starting AI2THOR Env HOT 1
- How long does it take to train rainbow HOT 3
- About task "place cup in microwave" HOT 13
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