pbar_evals = tqdm.tqdm(total=eval_count, desc="Evaluations")
for sample_size in configs['S']:
for rollout_max in configs['Roll-outs']:
for sig_lvl in configs['Significance']:
run_results = evaluations_per_config(
s_size=sample_size,
n_actions=configs['Actions'][0],
max_n_rollouts=rollout_max,
sig_lvl=sig_lvl,
max_policy_iter_per_run=10,
runs_per_config=10,
off_policy_explr=EXPLORE_LOGIC,
rollout_tracking=False,
dataset_tracking=False,
train_plot_tracking=False,
eval_summary_tracking=False,
show_experiment_eval_plot=False)
agg_results.append(run_results)
pbar_evals.update(1)
pbar_evals.close()
# Save the evaluation results
results_dfs = []
run_results = evaluations_per_config(
s_size=sample_size
#, init_state_path = configs['init_state_path'] # Use a pre-designed init state configs
,
n_actions=configs['Actions'][0],
max_n_rollouts=rollout_max,
sig_lvl=sig_lvl,
max_policy_iter_per_run=
10 # Maximum number of policy iterations per experiment
,
runs_per_config=
10 # Number of experiments per one parameter config
,
eval_runs_per_state=
100 # Episodes to generate from each init. state (during evaluation)
,
off_policy_explr=EXPLORE_LOGIC # What algorithm to use
,
rollout_tracking=False # Show rollout info.
,
dataset_tracking=False # Show train dataset
,
train_plot_tracking=False # Show model training plot
,
eval_summary_tracking=
False # Show a policy performance summary of evaluation runs
,
policy_behaviour_tracking=
False # Show/store policy action selections vs. pendulum angle plot
,
show_experiment_run_eval_summary_plot=
False # Show SR vs. action no. plot of exp. run
)
