Runner script to sparify reward models for PointMaze

Author: AdamGleave

runners/sparsify_point_maze.sh

@@ -0,0 +1,65 @@
+#!/usr/bin/env bash
+# Copyright 2020 Adam Gleave
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#      http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Script to sparsify pretrained reward models generated by `transfer_point_maze.sh`
+
+DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
+. ${DIR}/common.sh
+
+ENV_TRAIN="imitation/PointMazeLeftVel-v0"
+TRANSITION_P=0.05
+
+if [[ ${fast} == "true" ]]; then
+  # intended for debugging
+  COMPARISON_TIMESTEPS="fast"
+  PM_OUTPUT=${OUTPUT_ROOT}/transfer_point_maze_fast
+  SPARSE_OUTPUT=${OUTPUT_ROOT}/sparse_point_maze_fast
+else
+  COMPARISON_TIMESTEPS=""
+  EVAL_TIMESTEPS=100000
+  PM_OUTPUT=${OUTPUT_ROOT}/transfer_point_maze
+  SPARSE_OUTPUT=${OUTPUT_ROOT}/sparse_point_maze
+fi
+
+MIXED_POLICY_PATH=${TRANSITION_P}:random:dummy:ppo2:${PM_OUTPUT}/expert/train/policies/final
+for name in comparison_expert comparison_mixture comparison_random; do
+  if [[ ${name} == "comparison_expert" ]]; then
+    extra_flags="dataset_factory_kwargs.policy_type=ppo2 \
+                 dataset_factory_kwargs.policy_path=${PM_OUTPUT}/expert/train/policies/final"
+  elif [[ ${name} == "comparison_mixture" ]]; then
+    extra_flags="dataset_factory_kwargs.policy_type=mixture \
+                 dataset_factory_kwargs.policy_path=${MIXED_POLICY_PATH}"
+  elif [[ ${name} == "comparison_random" ]]; then
+    extra_flags=""
+  else
+    echo "BUG: unknown name ${name}"
+    exit 1
+  fi
+  parallel --header : --results ${SPARSE_OUTPUT}/parallel/${name} \
+    $(call_script "model_comparison" "with") \
+    env_name=${ENV_TRAIN} ${extra_flags} \
+    ellp_loss no_rescale target_reward_type=evaluating_rewards/Zero-v0 \
+    seed={seed} source_reward_type={source_reward_type} \
+    source_reward_path=${PM_OUTPUT}/reward/{source_reward_path}/{source_reward_suffix} \
+    ${COMPARISON_TIMESTEPS} log_dir=${SPARSE_OUTPUT}/${name}/{source_reward_path}/{seed} \
+    ::: source_reward_type evaluating_rewards/PointMazeGroundTruthWithCtrl-v0 \
+        evaluating_rewards/PointMazeGroundTruthNoCtrl-v0 \
+        evaluating_rewards/RewardModel-v0 evaluating_rewards/RewardModel-v0 \
+        imitation/RewardNet_unshaped-v0 imitation/RewardNet_unshaped-v0 \
+    :::+ source_reward_path withctrl noctrl preferences regress irl_state_only irl_state_action \
+    :::+ source_reward_suffix dummy dummy model model checkpoints/final/discrim/reward_net \
+                              checkpoints/final/discrim/reward_net \
+    ::: seed 0 1 2
+done