"framework": "tf",
# Tweak the default model provided automatically by RLlib,
# given the environment's observation- and action spaces.
"model": {
"fcnet_hiddens": [64, 64],
"fcnet_activation": "relu",
},
# Set up a separate evaluation worker set for the
# `algo.evaluate()` call after training (see below).
"evaluation_num_workers": 1,
# Only for evaluation runs, render the env.
"evaluation_config": {
"render_env": True,
},
}
# Create our RLlib Trainer.
algo = PPO(config=config)
# Run it for n training iterations. A training iteration includes
# parallel sample collection by the environment workers as well as
# loss calculation on the collected batch and a model update.
for _ in range(3):
print(algo.train())
# Evaluate the trained Trainer (and render each timestep to the shell's
# output).
algo.evaluate()
# __rllib-in-60s-end__
"framework": "tf",
# Tweak the default model provided automatically by RLlib,
# given the environment's observation- and action spaces.
"model": {
"fcnet_hiddens": [64, 64],
"fcnet_activation": "relu",
},
# Set up a separate evaluation worker set for the
# `trainer.evaluate()` call after training (see below).
"evaluation_num_workers": 1,
# Only for evaluation runs, render the env.
"evaluation_config": {
"render_env": True,
},
}
# Create our RLlib Trainer.
trainer = PPO(config=config)
# Run it for n training iterations. A training iteration includes
# parallel sample collection by the environment workers as well as
# loss calculation on the collected batch and a model update.
for _ in range(3):
print(trainer.train())
# Evaluate the trained Trainer (and render each timestep to the shell's
# output).
trainer.evaluate()
# __rllib-in-60s-end__
