pre-release 0.10.0

benchmarks/dueling_ddqn_with_per/README.md

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+# Dueling DDQN with Prioritized Experience Replay
+
+Each experiment uses 3 seeds and is trained for 10k environment steps.
+The parameters used for Dueling DDQN with PER are the same parameters as described in the [following paper](https://arxiv.org/abs/1511.05952).
+
+### Breakout Dueling DDQN with PER - single worker
+
+```bash
+python3 coach.py -p Atari_Dueling_DDQN_with_PER_OpenAI -lvl breakout
+```
+
+<img src="breakout_dueling_ddqn_with_per.png" alt="Breakout Dueling DDQN with PER" width="800"/>
+
+
+### Pong Dueling DDQN with PER - single worker
+
+```bash
+python3 coach.py -p Atari_Dueling_DDQN_with_PER_OpenAI -lvl pong
+```
+
+<img src="pong_dueling_ddqn_with_per.png" alt="Pong Dueling DDQN with PER" width="800"/>
+
+
+### Space Invaders Dueling DDQN with PER - single worker
+
+```bash
+python3 coach.py -p Atari_Dueling_DDQN_with_PER_OpenAI -lvl space_invaders
+```
+
+<img src="space_invaders_dueling_ddqn_with_per.png" alt="Space Invaders Dueling DDQN with PER" width="800"/>
+