I've used Ray RLlib's DQN to train in my custom simulator. It usually produced good results after 15 million steps.
After playing around with DQN for a while, I'm now trying to train A2C in the simulator. However, it's not even close to converging as you can see in the graph below. Usually, -50 is considered maximum in my simulator, which is mostly reached by 15 million steps with DQN.
The simulator is exactly the same for both DQN and A2C:
- 71 discrete observations
- 3 discrete actions.
I reckoned the environment doesn't have to change at all for either algorithm. Maybe I'm wrong there...
Can someone think of a reason why A2C isn't learning in my simulator?
Parameters for A2C:
(Same as the default configuration on Ray RLlib)
# Should use a critic as a baseline (otherwise don't use value baseline;
# required for using GAE).
"use_critic": True,
# If true, use the Generalized Advantage Estimator (GAE)
# with a value function, see https://arxiv.org/pdf/1506.02438.pdf.
"use_gae": True,
# Size of rollout batch
"rollout_fragment_length": 20,
# GAE(gamma) parameter
"lambda": 1.0,
# Max global norm for each gradient calculated by worker
"grad_clip": 40.0,
# Learning rate
"lr": 0.0001,
# Learning rate schedule
"lr_schedule": None,
# Value Function Loss coefficient
"vf_loss_coeff": 0.5,
# Entropy coefficient
"entropy_coeff": 0.01,
# Min time per iteration
"min_iter_time_s": 10,
# Workers sample async. Note that this increases the effective
# rollout_fragment_length by up to 5x due to async buffering of batches.
"sample_async": False,
# Switch on Trajectory View API for A2/3C by default.
# NOTE: Only supported for PyTorch so far.
"_use_trajectory_view_api": True,
# A2C supports microbatching, in which we accumulate gradients over
# batch of this size until the train batch size is reached. This allows
# training with batch sizes much larger than can fit in GPU memory.
# To enable, set this to a value less than the train batch size.
"microbatch_size": None
