add ARS to train/eval Minitaur

examples/pybullet/gym/pybullet_envs/ARS/logz.py

@@ -0,0 +1,104 @@
+# Code in this file is copied and adapted from 
+# https://github.com/berkeleydeeprlcourse
+
+import json
+
+"""
+
+Some simple logging functionality, inspired by rllab's logging.
+Assumes that each diagnostic gets logged each iteration
+
+Call logz.configure_output_dir() to start logging to a 
+tab-separated-values file (some_folder_name/log.txt)
+
+"""
+
+import os.path as osp, shutil, time, atexit, os, subprocess
+
+color2num = dict(
+    gray=30,
+    red=31,
+    green=32,
+    yellow=33,
+    blue=34,
+    magenta=35,
+    cyan=36,
+    white=37,
+    crimson=38
+)
+
+def colorize(string, color, bold=False, highlight=False):
+    attr = []
+    num = color2num[color]
+    if highlight: num += 10
+    attr.append(str(num))
+    if bold: attr.append('1')
+    return '\x1b[%sm%s\x1b[0m' % (';'.join(attr), string)
+
+class G(object):
+    output_dir = None
+    output_file = None
+    first_row = True
+    log_headers = []
+    log_current_row = {}
+
+def configure_output_dir(d=None):
+    """
+    Set output directory to d, or to /tmp/somerandomnumber if d is None
+    """
+    G.first_row = True
+    G.log_headers = []
+    G.log_current_row = {}
+    
+    G.output_dir = d or "/tmp/experiments/%i"%int(time.time())
+    if not osp.exists(G.output_dir):
+        os.makedirs(G.output_dir)
+    G.output_file = open(osp.join(G.output_dir, "log.txt"), 'w')
+    atexit.register(G.output_file.close)
+    print(colorize("Logging data to %s"%G.output_file.name, 'green', bold=True))
+
+def log_tabular(key, val):
+    """
+    Log a value of some diagnostic
+    Call this once for each diagnostic quantity, each iteration
+    """
+    if G.first_row:
+        G.log_headers.append(key)
+    else:
+        assert key in G.log_headers, "Trying to introduce a new key %s that you didn't include in the first iteration"%key
+    assert key not in G.log_current_row, "You already set %s this iteration. Maybe you forgot to call dump_tabular()"%key
+    G.log_current_row[key] = val
+
+    
+def save_params(params):
+    with open(osp.join(G.output_dir, "params.json"), 'w') as out:
+        out.write(json.dumps(params, separators=(',\n','\t:\t'), sort_keys=True))
+
+
+def dump_tabular():
+    """
+    Write all of the diagnostics from the current iteration
+    """
+    vals = []
+    key_lens = [len(key) for key in G.log_headers]
+    max_key_len = max(15,max(key_lens))
+    keystr = '%'+'%d'%max_key_len
+    fmt = "| " + keystr + "s | %15s |"
+    n_slashes = 22 + max_key_len
+    print("-"*n_slashes)
+    for key in G.log_headers:
+        val = G.log_current_row.get(key, "")
+        if hasattr(val, "__float__"): valstr = "%8.3g"%val
+        else: valstr = val
+        print(fmt%(key, valstr))
+        vals.append(val)
+    print("-"*n_slashes)
+    if G.output_file is not None:
+        if G.first_row:
+            G.output_file.write("\t".join(G.log_headers))
+            G.output_file.write("\n")
+        G.output_file.write("\t".join(map(str,vals)))
+        G.output_file.write("\n")
+        G.output_file.flush()
+    G.log_current_row.clear()
+    G.first_row=False