rollout: A simulation of a policy in an environment.
- command line examples
train:rllib train --run DQN --env CartPole-v0 # --eager [--trace] for eager execution
tensorboardtensorboard --logdir=~/ray_resultsevaluatingrllib rollout \ ~/ray_results/default/DQN_CartPole-v0_0upjmdgr0/checkpoint_1/checkpoint-1 \ --run DQN --env CartPole-v0 --steps 10000 - configuration parameters
You can control the degree of parallelism used by setting the num_workers hyperparameter for most algorithms. The number of GPUs the driver should use can be set via the num_gpus option. Similarly, the resource allocation to workers can be controlled via num_cpus_per_worker, *num_gpus_per_worker, and custom_resources_per_worker. The number of GPUs can be a fractional quantity to allocate only a fraction of a GPU. For example, with DQN you can pack five trainers onto one GPU by setting num_gpus: 0.2.
Agents are rewarded based on minimum agent distance to each landmark, penalized for collisions @property transfers method as attribute.