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@@ -471,7 +471,12 @@ RLlib's MBMPO implementation is a Dyna-styled model-based RL method that learns
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Additional statistics are logged in MBMPO. Each MBMPO iteration corresponds to multiple MAML iterations, and ``MAMLIter$i$_DynaTrajInner_$j$_episode_reward_mean`` measures the agent's returns across the dynamics models at iteration ``i`` of MAML and step ``j`` of inner adaptation. Examples can be seen `here <https://github.com/ray-project/rl-experiments/tree/master/mbmpo>`__.
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Tuned examples: `HalfCheetah <https://github.com/ray-project/ray/blob/master/rllib/tuned_examples/mbmpo/halfcheetah-mbmpo.yaml>`__, `Hopper <https://github.com/ray-project/ray/blob/master/rllib/tuned_examples/mbmpo/hopper-mbmpo.yaml>`__
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Tuned examples (continuous actions):
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`Pendulum-v0 <https://github.com/ray-project/ray/blob/master/rllib/tuned_examples/mbmpo/pendulum-mbmpo.yaml>`__,
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`HalfCheetah <https://github.com/ray-project/ray/blob/master/rllib/tuned_examples/mbmpo/halfcheetah-mbmpo.yaml>`__,
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`Hopper <https://github.com/ray-project/ray/blob/master/rllib/tuned_examples/mbmpo/hopper-mbmpo.yaml>`__,
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Tuned examples (discrete actions):
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`CartPole-v0 <https://github.com/ray-project/ray/blob/master/rllib/tuned_examples/mbmpo/cartpole-mbmpo.yaml>`__
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**MuJoCo results @100K steps:** `more details <https://github.com/ray-project/rl-experiments>`__
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